{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00048142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device cuda:0\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "from torchvision.transforms import autoaugment\n",
    "\n",
    "\n",
    "# 忽略烦人的红色提示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 获取计算硬件\n",
    "# 有 GPU 就用 GPU，没有就用 CPU\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print('device', device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36ebca83",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei']  # 用来正常显示中文标签 \n",
    "plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3074a020",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transform = transforms.Compose([\n",
    "    transforms.Resize(256),\n",
    "    transforms.RandomResizedCrop(224), # 随机裁剪\n",
    "    transforms.RandomHorizontalFlip(), # 随机水平翻转\n",
    "    transforms.RandomVerticalFlip(), # 随机垂直翻转\n",
    "    autoaugment.TrivialAugmentWide(),  # 自动数据增强\n",
    "    transforms.ToTensor(), # 转换为张量\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "    transforms.RandomErasing(p=0.5, scale=(0.02, 0.1)),  # 随机擦除\n",
    "])\n",
    "val_transform = transforms.Compose([\n",
    "    transforms.Resize((256,256)),\n",
    "    transforms.CenterCrop(224), # 中心裁剪\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d437795",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = r'C:\\Users\\24566\\Desktop\\cv\\fruit81_split'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd6f5fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = os.path.join(dataset, 'train')\n",
    "val = os.path.join(dataset, 'val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d55693df",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 3] 系统找不到指定的路径。: 'C:\\\\Users\\\\24566\\\\Desktop\\\\cv\\\\fruit81_split\\\\train'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m train_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mdatasets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mImageFolder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_transform\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m val_dataset \u001b[38;5;241m=\u001b[39m datasets\u001b[38;5;241m.\u001b[39mImageFolder(root\u001b[38;5;241m=\u001b[39mval, transform\u001b[38;5;241m=\u001b[39mval_transform)\n",
      "File \u001b[1;32md:\\anaconda3\\envs\\cv\\lib\\site-packages\\torchvision\\datasets\\folder.py:328\u001b[0m, in \u001b[0;36mImageFolder.__init__\u001b[1;34m(self, root, transform, target_transform, loader, is_valid_file, allow_empty)\u001b[0m\n\u001b[0;32m    319\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[0;32m    320\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    321\u001b[0m     root: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    326\u001b[0m     allow_empty: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    327\u001b[0m ):\n\u001b[1;32m--> 328\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    329\u001b[0m \u001b[43m        \u001b[49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    330\u001b[0m \u001b[43m        \u001b[49m\u001b[43mloader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    331\u001b[0m \u001b[43m        \u001b[49m\u001b[43mIMG_EXTENSIONS\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_valid_file\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    332\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    333\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtarget_transform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_transform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    334\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_valid_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_valid_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    335\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_empty\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_empty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    336\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    337\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimgs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msamples\n",
      "File \u001b[1;32md:\\anaconda3\\envs\\cv\\lib\\site-packages\\torchvision\\datasets\\folder.py:149\u001b[0m, in \u001b[0;36mDatasetFolder.__init__\u001b[1;34m(self, root, loader, extensions, transform, target_transform, is_valid_file, allow_empty)\u001b[0m\n\u001b[0;32m    138\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[0;32m    139\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    140\u001b[0m     root: Union[\u001b[38;5;28mstr\u001b[39m, Path],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    146\u001b[0m     allow_empty: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    147\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    148\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(root, transform\u001b[38;5;241m=\u001b[39mtransform, target_transform\u001b[38;5;241m=\u001b[39mtarget_transform)\n\u001b[1;32m--> 149\u001b[0m     classes, class_to_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_classes\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    150\u001b[0m     samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_dataset(\n\u001b[0;32m    151\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroot,\n\u001b[0;32m    152\u001b[0m         class_to_idx\u001b[38;5;241m=\u001b[39mclass_to_idx,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    155\u001b[0m         allow_empty\u001b[38;5;241m=\u001b[39mallow_empty,\n\u001b[0;32m    156\u001b[0m     )\n\u001b[0;32m    158\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader \u001b[38;5;241m=\u001b[39m loader\n",
      "File \u001b[1;32md:\\anaconda3\\envs\\cv\\lib\\site-packages\\torchvision\\datasets\\folder.py:234\u001b[0m, in \u001b[0;36mDatasetFolder.find_classes\u001b[1;34m(self, directory)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfind_classes\u001b[39m(\u001b[38;5;28mself\u001b[39m, directory: Union[\u001b[38;5;28mstr\u001b[39m, Path]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[\u001b[38;5;28mstr\u001b[39m], Dict[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mint\u001b[39m]]:\n\u001b[0;32m    208\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Find the class folders in a dataset structured as follows::\u001b[39;00m\n\u001b[0;32m    209\u001b[0m \n\u001b[0;32m    210\u001b[0m \u001b[38;5;124;03m        directory/\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[38;5;124;03m        (Tuple[List[str], Dict[str, int]]): List of all classes and dictionary mapping each class to an index.\u001b[39;00m\n\u001b[0;32m    233\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 234\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfind_classes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\anaconda3\\envs\\cv\\lib\\site-packages\\torchvision\\datasets\\folder.py:41\u001b[0m, in \u001b[0;36mfind_classes\u001b[1;34m(directory)\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfind_classes\u001b[39m(directory: Union[\u001b[38;5;28mstr\u001b[39m, Path]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[\u001b[38;5;28mstr\u001b[39m], Dict[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mint\u001b[39m]]:\n\u001b[0;32m     37\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Finds the class folders in a dataset.\u001b[39;00m\n\u001b[0;32m     38\u001b[0m \n\u001b[0;32m     39\u001b[0m \u001b[38;5;124;03m    See :class:`DatasetFolder` for details.\u001b[39;00m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m     classes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(entry\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m entry \u001b[38;5;129;01min\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscandir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m entry\u001b[38;5;241m.\u001b[39mis_dir())\n\u001b[0;32m     42\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m classes:\n\u001b[0;32m     43\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find any class folder in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdirectory\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] 系统找不到指定的路径。: 'C:\\\\Users\\\\24566\\\\Desktop\\\\cv\\\\fruit81_split\\\\train'"
     ]
    }
   ],
   "source": [
    "train_dataset = datasets.ImageFolder(root=train, transform=train_transform)\n",
    "val_dataset = datasets.ImageFolder(root=val, transform=val_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "915a488e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集图像数量 14313\n",
      "类别个数 81\n",
      "各类别名称 ['人参果', '佛手瓜', '哈密瓜', '圣女果', '山楂', '山竹', '无花果', '木瓜', '李子', '杏', '杨桃', '杨梅', '枇杷', '枣', '柚子', '柠檬', '柿子', '树莓', '桂圆', '桑葚', '梨', '椰子', '榴莲', '樱桃', '橘子', '毛丹', '水蜜桃', '沃柑', '沙果', '沙棘', '油桃', '牛油果', '猕猴桃', '甘蔗', '甜瓜-伊丽莎白', '甜瓜-白', '甜瓜-绿', '甜瓜-金', '番石榴-百', '番石榴-红', '白兰瓜', '白心火龙果', '白萝卜', '百香果', '石榴', '砂糖橘', '粑粑柑', '红心火龙果', '红苹果', '羊奶果', '羊角蜜', '胡萝卜', '脐橙', '腰果', '芒果', '芦柑', '草莓', '荔枝', '莲雾', '菠萝', '菠萝莓', '菠萝蜜', '葡萄-白', '葡萄-红', '蓝莓', '蛇皮果', '蟠桃', '血橙', '西柚', '西梅', '西瓜', '西红柿', '车厘子', '酸角', '金桔', '青柠', '青苹果', '香橼', '香蕉', '黄桃', '黑莓']\n"
     ]
    }
   ],
   "source": [
    "print('训练集图像数量', len(train_dataset))\n",
    "print('类别个数', len(train_dataset.classes))\n",
    "print('各类别名称', train_dataset.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9c6f852",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集图像数量 6997\n",
      "类别个数 81\n",
      "各类别名称 ['人参果', '佛手瓜', '哈密瓜', '圣女果', '山楂', '山竹', '无花果', '木瓜', '李子', '杏', '杨桃', '杨梅', '枇杷', '枣', '柚子', '柠檬', '柿子', '树莓', '桂圆', '桑葚', '梨', '椰子', '榴莲', '樱桃', '橘子', '毛丹', '水蜜桃', '沃柑', '沙果', '沙棘', '油桃', '牛油果', '猕猴桃', '甘蔗', '甜瓜-伊丽莎白', '甜瓜-白', '甜瓜-绿', '甜瓜-金', '番石榴-百', '番石榴-红', '白兰瓜', '白心火龙果', '白萝卜', '百香果', '石榴', '砂糖橘', '粑粑柑', '红心火龙果', '红苹果', '羊奶果', '羊角蜜', '胡萝卜', '脐橙', '腰果', '芒果', '芦柑', '草莓', '荔枝', '莲雾', '菠萝', '菠萝莓', '菠萝蜜', '葡萄-白', '葡萄-红', '蓝莓', '蛇皮果', '蟠桃', '血橙', '西柚', '西梅', '西瓜', '西红柿', '车厘子', '酸角', '金桔', '青柠', '青苹果', '香橼', '香蕉', '黄桃', '黑莓']\n"
     ]
    }
   ],
   "source": [
    "print('测试集图像数量', len(val_dataset))\n",
    "print('类别个数', len(val_dataset.classes))\n",
    "print('各类别名称', val_dataset.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "072354a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['人参果',\n",
       " '佛手瓜',\n",
       " '哈密瓜',\n",
       " '圣女果',\n",
       " '山楂',\n",
       " '山竹',\n",
       " '无花果',\n",
       " '木瓜',\n",
       " '李子',\n",
       " '杏',\n",
       " '杨桃',\n",
       " '杨梅',\n",
       " '枇杷',\n",
       " '枣',\n",
       " '柚子',\n",
       " '柠檬',\n",
       " '柿子',\n",
       " '树莓',\n",
       " '桂圆',\n",
       " '桑葚',\n",
       " '梨',\n",
       " '椰子',\n",
       " '榴莲',\n",
       " '樱桃',\n",
       " '橘子',\n",
       " '毛丹',\n",
       " '水蜜桃',\n",
       " '沃柑',\n",
       " '沙果',\n",
       " '沙棘',\n",
       " '油桃',\n",
       " '牛油果',\n",
       " '猕猴桃',\n",
       " '甘蔗',\n",
       " '甜瓜-伊丽莎白',\n",
       " '甜瓜-白',\n",
       " '甜瓜-绿',\n",
       " '甜瓜-金',\n",
       " '番石榴-百',\n",
       " '番石榴-红',\n",
       " '白兰瓜',\n",
       " '白心火龙果',\n",
       " '白萝卜',\n",
       " '百香果',\n",
       " '石榴',\n",
       " '砂糖橘',\n",
       " '粑粑柑',\n",
       " '红心火龙果',\n",
       " '红苹果',\n",
       " '羊奶果',\n",
       " '羊角蜜',\n",
       " '胡萝卜',\n",
       " '脐橙',\n",
       " '腰果',\n",
       " '芒果',\n",
       " '芦柑',\n",
       " '草莓',\n",
       " '荔枝',\n",
       " '莲雾',\n",
       " '菠萝',\n",
       " '菠萝莓',\n",
       " '菠萝蜜',\n",
       " '葡萄-白',\n",
       " '葡萄-红',\n",
       " '蓝莓',\n",
       " '蛇皮果',\n",
       " '蟠桃',\n",
       " '血橙',\n",
       " '西柚',\n",
       " '西梅',\n",
       " '西瓜',\n",
       " '西红柿',\n",
       " '车厘子',\n",
       " '酸角',\n",
       " '金桔',\n",
       " '青柠',\n",
       " '青苹果',\n",
       " '香橼',\n",
       " '香蕉',\n",
       " '黄桃',\n",
       " '黑莓']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各类别名称\n",
    "class_names = train_dataset.classes\n",
    "n_class = len(class_names)\n",
    "class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6c4f022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'人参果': 0,\n",
       " '佛手瓜': 1,\n",
       " '哈密瓜': 2,\n",
       " '圣女果': 3,\n",
       " '山楂': 4,\n",
       " '山竹': 5,\n",
       " '无花果': 6,\n",
       " '木瓜': 7,\n",
       " '李子': 8,\n",
       " '杏': 9,\n",
       " '杨桃': 10,\n",
       " '杨梅': 11,\n",
       " '枇杷': 12,\n",
       " '枣': 13,\n",
       " '柚子': 14,\n",
       " '柠檬': 15,\n",
       " '柿子': 16,\n",
       " '树莓': 17,\n",
       " '桂圆': 18,\n",
       " '桑葚': 19,\n",
       " '梨': 20,\n",
       " '椰子': 21,\n",
       " '榴莲': 22,\n",
       " '樱桃': 23,\n",
       " '橘子': 24,\n",
       " '毛丹': 25,\n",
       " '水蜜桃': 26,\n",
       " '沃柑': 27,\n",
       " '沙果': 28,\n",
       " '沙棘': 29,\n",
       " '油桃': 30,\n",
       " '牛油果': 31,\n",
       " '猕猴桃': 32,\n",
       " '甘蔗': 33,\n",
       " '甜瓜-伊丽莎白': 34,\n",
       " '甜瓜-白': 35,\n",
       " '甜瓜-绿': 36,\n",
       " '甜瓜-金': 37,\n",
       " '番石榴-百': 38,\n",
       " '番石榴-红': 39,\n",
       " '白兰瓜': 40,\n",
       " '白心火龙果': 41,\n",
       " '白萝卜': 42,\n",
       " '百香果': 43,\n",
       " '石榴': 44,\n",
       " '砂糖橘': 45,\n",
       " '粑粑柑': 46,\n",
       " '红心火龙果': 47,\n",
       " '红苹果': 48,\n",
       " '羊奶果': 49,\n",
       " '羊角蜜': 50,\n",
       " '胡萝卜': 51,\n",
       " '脐橙': 52,\n",
       " '腰果': 53,\n",
       " '芒果': 54,\n",
       " '芦柑': 55,\n",
       " '草莓': 56,\n",
       " '荔枝': 57,\n",
       " '莲雾': 58,\n",
       " '菠萝': 59,\n",
       " '菠萝莓': 60,\n",
       " '菠萝蜜': 61,\n",
       " '葡萄-白': 62,\n",
       " '葡萄-红': 63,\n",
       " '蓝莓': 64,\n",
       " '蛇皮果': 65,\n",
       " '蟠桃': 66,\n",
       " '血橙': 67,\n",
       " '西柚': 68,\n",
       " '西梅': 69,\n",
       " '西瓜': 70,\n",
       " '西红柿': 71,\n",
       " '车厘子': 72,\n",
       " '酸角': 73,\n",
       " '金桔': 74,\n",
       " '青柠': 75,\n",
       " '青苹果': 76,\n",
       " '香橼': 77,\n",
       " '香蕉': 78,\n",
       " '黄桃': 79,\n",
       " '黑莓': 80}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 映射关系：类别 到 索引号\n",
    "train_dataset.class_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2858497b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: '人参果',\n",
       " 1: '佛手瓜',\n",
       " 2: '哈密瓜',\n",
       " 3: '圣女果',\n",
       " 4: '山楂',\n",
       " 5: '山竹',\n",
       " 6: '无花果',\n",
       " 7: '木瓜',\n",
       " 8: '李子',\n",
       " 9: '杏',\n",
       " 10: '杨桃',\n",
       " 11: '杨梅',\n",
       " 12: '枇杷',\n",
       " 13: '枣',\n",
       " 14: '柚子',\n",
       " 15: '柠檬',\n",
       " 16: '柿子',\n",
       " 17: '树莓',\n",
       " 18: '桂圆',\n",
       " 19: '桑葚',\n",
       " 20: '梨',\n",
       " 21: '椰子',\n",
       " 22: '榴莲',\n",
       " 23: '樱桃',\n",
       " 24: '橘子',\n",
       " 25: '毛丹',\n",
       " 26: '水蜜桃',\n",
       " 27: '沃柑',\n",
       " 28: '沙果',\n",
       " 29: '沙棘',\n",
       " 30: '油桃',\n",
       " 31: '牛油果',\n",
       " 32: '猕猴桃',\n",
       " 33: '甘蔗',\n",
       " 34: '甜瓜-伊丽莎白',\n",
       " 35: '甜瓜-白',\n",
       " 36: '甜瓜-绿',\n",
       " 37: '甜瓜-金',\n",
       " 38: '番石榴-百',\n",
       " 39: '番石榴-红',\n",
       " 40: '白兰瓜',\n",
       " 41: '白心火龙果',\n",
       " 42: '白萝卜',\n",
       " 43: '百香果',\n",
       " 44: '石榴',\n",
       " 45: '砂糖橘',\n",
       " 46: '粑粑柑',\n",
       " 47: '红心火龙果',\n",
       " 48: '红苹果',\n",
       " 49: '羊奶果',\n",
       " 50: '羊角蜜',\n",
       " 51: '胡萝卜',\n",
       " 52: '脐橙',\n",
       " 53: '腰果',\n",
       " 54: '芒果',\n",
       " 55: '芦柑',\n",
       " 56: '草莓',\n",
       " 57: '荔枝',\n",
       " 58: '莲雾',\n",
       " 59: '菠萝',\n",
       " 60: '菠萝莓',\n",
       " 61: '菠萝蜜',\n",
       " 62: '葡萄-白',\n",
       " 63: '葡萄-红',\n",
       " 64: '蓝莓',\n",
       " 65: '蛇皮果',\n",
       " 66: '蟠桃',\n",
       " 67: '血橙',\n",
       " 68: '西柚',\n",
       " 69: '西梅',\n",
       " 70: '西瓜',\n",
       " 71: '西红柿',\n",
       " 72: '车厘子',\n",
       " 73: '酸角',\n",
       " 74: '金桔',\n",
       " 75: '青柠',\n",
       " 76: '青苹果',\n",
       " 77: '香橼',\n",
       " 78: '香蕉',\n",
       " 79: '黄桃',\n",
       " 80: '黑莓'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 映射关系：索引号 到 类别\n",
    "idx_to_labels = {y:x for x,y in train_dataset.class_to_idx.items()}\n",
    "idx_to_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cd57a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存为本地的 npy 文件\n",
    "np.save('idx_to_labels.npy', idx_to_labels)\n",
    "np.save('labels_to_idx.npy', train_dataset.class_to_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eec4402",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dce1ce51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset ImageFolder\n",
      "    Number of datapoints: 6997\n",
      "    Root location: C:\\Users\\24566\\Desktop\\cv\\fruit81_split\\val\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               Resize(size=(256, 256), interpolation=bilinear, max_size=None, antialias=True)\n",
      "               CenterCrop(size=(224, 224))\n",
      "               ToTensor()\n",
      "               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
      "           )\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "# 训练集的数据加载器\n",
    "train_loader = DataLoader(train_dataset,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          shuffle=True,\n",
    "                          num_workers=1\n",
    "                         )\n",
    "\n",
    "# 测试集的数据加载器\n",
    "test_loader = DataLoader(val_dataset,\n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         shuffle=False,\n",
    "                         num_workers=1\n",
    "                        )\n",
    "print(test_loader.dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75604f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 3, 224, 224])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看一个batch 的图片和标签\n",
    "images, labels = next(iter(train_loader))  # 获取一个batch的图片和标签\n",
    "images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87714c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 4, 12, 41, 52, 48, 27,  2, 41, 16, 53, 55, 44, 13, 46, 64, 45, 67, 30,\n",
       "         2, 18, 41, 33, 71, 57, 24, 51, 62,  9,  7, 27, 74,  4])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "933318b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torch.Tensor'>\n",
      "torch.Size([3, 224, 224])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化一个batch\n",
    "print(type(images))\n",
    "print(images[0].shape) # images[0]是查看第0张图片的shape\n",
    "plt.hist(images[5].flatten(), bins=50) # 直方图，像素分布\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea2f4261",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.2489083].\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGvCAYAAAAUi6H2AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs/XmcXHd1542/71J7V++r9tWyJFsWxjLGYBuC2cFLIMSDgwcnzAM8jEnwL05CyOTBIbEz85vwSwIDxMALhiGQH5lJcMIWEhYHDDbesBGSbcnapZbU+1brXZ4/zj19b7dacrdU3V3dup/Xq9TqW9W3bt2q+n7uOedzPsfwfd8nRowYMWLEWGSYi30AMWLEiBEjBsSEFCNGjBgx6gQxIcWIESNGjLpATEgxYsSIEaMuEBNSjBgxYsSoC8SEFCNGjBgx6gIxIcWIESNGjLpATEgxYsSIEaMuEBNSjBgxYsSoC8SEFCPGOfDRj36UV73qVXP+m8suu6wmz//DH/4QwzAueD+jo6OsWbOGL37xixd+UDFizBNiQooR4yLAPffcw9GjRxf7MGLEOCdiQooRY5nj+9//Pl/4whfIZDKLfSgxYpwTMSHFiLGMMTExwXve8x5+//d/n87OzsU+nBgxzomYkGLEOA/87d/+Ldu2bSObzXLppZfy1a9+9YzH/Mmf/AktLS20t7fzx3/8x0SN9QuFAh/84Afp7OykpaWFX//1X6evr2/Ox3Ho0CEMw+DrX//6jPf/wR/8Afl8nj/+4z+e875jxFhoxIQUI8Yc8fDDD/Oud72LN73pTXz3u9/l9ttv54477uDAgQOTj3nuuef453/+Z/7u7/6O3/u93+O+++7jb/7mbybvf9/73sfXv/51PvWpT/GVr3yF3bt386u/+qtzPpYVK1bw2GOPzSi8+NGPfsRnP/tZ/uf//J8kEonzeq0xYiwk7MU+gBgxlhoymQx/8zd/w2/+5m9iWRaXXHIJH/vYx3j88cfZsGEDAK7r8tWvfpVNmzbx+te/nqeffpq//Mu/5H3vex8HDx7ky1/+Mv/wD//ALbfcAoDjONx0000cPHiQ9evXz/pYkskkV1111Rnbi8Uiv/Vbv8Uf/uEfsnPnzlq87Bgx5h0xIcWIMUdceeWVFItFfvd3f5eHH36Yp59+GsdxKBQKk49ZuXIlmzZtmvz96quv5u///u9xXZfdu3fj+z633nrrGfvet2/fnAjpbPgv/+W/kM/n+cM//MML3leMGAuFOGUXI8Yc8alPfYpXv/rVk3WgvXv3smbNmimPMc2pXy3LsvB9f0od6Tvf+Q5PPfXUlNs111xTk2P83//7f/Pkk0+SSCQwDAPDMDh8+DB33nlnTfqaYsSYD8QRUowYc8RnP/tZbrvttsmaUF9fH4ODg1Mec+zYMY4cOTJJVI8//jhr167Ftm22b98OQLlcnkyn9fb2cs899/D7v//7XH755Rd8jN/61reoVCpTtr3pTW/ife97HzfddNMF7z9GjPlATEgxYswR7e3tPPLII3zve9/j5MmTfOxjH2NsbAzHcaY87rbbbuPee+/lF7/4BV/5ylf4b//tvwGwYcMG3vWud/GBD3yAsbExVqxYwf33388vf/lLPv3pT8/pWCqVCs888wybNm2iubl5cvu2bdvOeGwymWTNmjVxTSlG3SJO2cWIMUd84hOfoKuri5tuuomPfexjvOc97+Gqq67ixz/+8eRjtm7dynXXXcfb3/52Pv7xj/NHf/RHfPCDH5y8/zOf+Qy33norH/rQh7jpppuwLIt/+7d/I5/Pz+lYTpw4wa5du/jhD39Yq5cXI8aiwfCjSe0YMWLEiBFjkRBHSDFixIgRoy4QE1KMGDFixKgLxIQUI0aMGDHqAjEhxYgRI0aMusCCENLu3bvZtWsXLS0t3HPPPcQ6ihgxYsSIMR3zTkjlcpm3vvWtvPSlL+Xxxx9nz5498dTKGDFixIhxBuZd9v31r3+d3/zN3+TYsWNks1mefvppPvCBD0zp2TgXPM/jxIkT5PP52PIkRowYMZYYfN+fbACfbqk1HfPu1PD0009zzTXXkM1mAdixYwd79uw56+PL5TLlcnny9+PHj8/YdR4jRowYMZYOjh49yqpVq875mHknpNHR0SnuxYZhYFkWQ0NDtLS0nPH4+++/n3vvvXe+D2vJIwvkU/CaWyG/KoG9oYHv/K9xXvh5FYqz2UMT8CagE2gFPgccnb8DXkJIp3Jcsf063vT6V/HOX38zVIfxKgUKQ6fwfAMPk3JxDN+rYFllcq095Fq6yHd1YacasOzWGkTzPuACBmFmfeEyBHfeeSf/8A//sGDPF2P5YzYuJPNOSLZtk0qlpmxLp9MUCoUZCenDH/4wd9999+Tvo6OjrF69er4Pc8kh1QiJLPzkEUh3uzSNTDDU50Llxf8WehAiagFGgMPAxDwe7dKCYRjYVgIDA7/qkc/msDI2GbdAuVygXCliMo5nevipFA2tTbSu6CbXvBrLTmMYyRoRkr6ZBvJVXThR7B/8wR/wG7/xG1O23XfffTz++OMLdgwxlhdm852Yd0JqbW1l9+7dU7aNjY2RTCZnfHwqlTqDwGLMABtcC04chWTBozlToTCKXFSfARPIAw5QAtqCWxnoRyKj0sIc9xKA73uUK0UmJsYZHh4hk0xjWQamYWD4Ll61iFct4OLhJCw8A4yEhWmmMM1afXb9yA3Aivx//iOlXbt2sWvXrvBofJ9vfOMbnD59+ozHOo5Db29vrJ6NccGYd0LatWsXn/vc5yZ/P3ToEOVymdbW1vl+6mWN4SFgGHwPSn1w6sfgO2d7dBPwfmAf8O/A5UAG+BYwDhQ4C5NdlCiVC/x890M05h1WdjVx/csvo71ZaqB4HjgulUoZx3NxfJ/i2CiF0VGyOQ9r3o7KRQhp/p7hxfCXf/mXVKvVM7Y///zzvOY1r5kyoDBGjPPBvBPS9ddfz8jICF/60pe44447+PM//3NuvPFGLGvxvljLAX704tkH/8x1IkA70IUQUAuwBjgJVIFRJEqqDzKykpDOwPoNYNlgBBkqz4dq1eR0r8/Ayfm/CjcNg0wiRUM6RVMuSbFQYNz2aEnbeI6NW7UoTvh4ngeej1OpUCoV8X0X8JAI5nyjGD/Yhxf8X/dlXsA+LxyGYZy1BrB27Vp+67d+a4oYqb+/n69//etyjmLEmCUWpIb0wAMP8M53vpN77rkH13V56KGH5vtpY0xiLUJCAM3AVuC7CCnVF+wMNHfDr7wZUmkwE7LddQ1GJmwef8hl4OT8k6dlWnTk2uhpaWVldxOF8XF8p0TXxg5MUhh+gsKoEFLFhUqlTKEwhudVESI534stJVuX8CJByUhv9Yeenh7++q//esq2p556im9961tTSCpO6cV4MSzIgL5bbrmFffv28fjjj3PttdfS0dGxEE8bAyB9JZjroPBDoA84DQye808WC9UJqIwBPuy4dD1XX7kFaOfgsVH+n7/+Fr19CxPJOa5L7+gp9h8/yjN7D/CqV15Jd3sjCW+cSnWCSnkcP5XHSts0NLXTvmoDbT3rSCRzXFhKbXrdSLe5F7jfhcfGjRv5p3/6pykR0p49e/jd3/3dOGqKcVYs2MTYlStXsnLlyoV6uosYJhIJ2YAFZhpMFziBkFHfIh7bueE5UC3D6AjYVpIVPXkyydVU3WFKro+zQOuYj0+xWqJ/eJhDR3rxfBHbmO4ElgmmCVYyA1YSI5cn3dBMJt+Cic2Fp9VmIiVm+L2+0djYyGtf+9op2zo6OtiyZQuuG15YFAoFjh07ttCHF6NOEY8wX3bIAe8GowfMLih+Evw9SB2p/jE2Cv/0IJDYR+u6g1y9+e3kGgxe9gqfn41C/76FO5aDB48x1v9DXrL9MhJGiu2b20kkEjSkM6TLLiXPZJw0+Cau72IbF0pHBhIJVQlTdvWbqpsrrrjiCh599NEp2x566CFuuummOJ0XA4gJaZlhFRirIfNKcMeh8hz4g4iKbhgRMNQ3PBcmhuHgfo+f/rTK5u4BXMOYInJYKJSqJQbHBzh85AgdrQ1s29SOaacwUhlSngu+iWOkSVpJbOwaSA4MQiGDOe3n0rfNsizrDGHEli1b+NCHPjSFkA4cOMCDDz640IcXow4QE9KywkYwroT8jVD+MZQ/j9SLPGBgkY9tdvA9KI/BC88CKXjja/rINRp4nr/gWauKU6Lqljlw8CBtjTn8V70Ew05jGA5J18XwTDw7R8pOYdckXadQ5rVquM/6xObNm/mLv/iLKdu++c1v8o1vfGPKNt/349rTRYCYkJYVNoF5BTTYYAwCP2epOjBccbXFr78vSaq7wuHDPt/8DAyd2ZM5r2hIZWjJNvKKl+3kpVdsw/SrSBdsjnRbE8lEhkymBSuZAiNJ7cgj+rVc3oQ0E6655hp+8IMfTNn2/e9/n49+9KOLc0AxFgwxIS1pGIgDQwJIIg1JQ+DuBu8FxBZoaSKTNWjtBCPhUqr4nDoK1QU2kzAwME2TluZGWlsagwjNBNPCTGUxUznINtbYhX55pOcuBG1tbVx33XVTthUKBXbs2DFl2+DgYCyIWGaICWlJIwm8CliB9Bt9Ddz/BYdN8Ouj2fV8cfyYw09/4tL9pippyxDh4DCzNI6tDcbKBQrVEmWngu8buI4FCQvTsiCZgsTM9lcxao8bb7zxDEHEl770Jd773vcu0hHFmA/EhLRkYQMZsNaCnwTvEBIRVZeaQnhGHDsGD//IZ9v2QTwMtl7v0/sM9O1d2OPwfJ+jvb0cPNZBa3MDSdMgYVnYnoGxDM7zUoFlWWe4u1x11VX80R/90ZRtjz/+ON/5zncW8tBi1BAxIS1ZpIA8WKvBHwbvMWBskY+pdjh6FHpLcO3rBmnrgW3XgTO88IQEcKT3JB0dbWy9ZCPYNoZnYHlgxDX2RcWVV17JlVdeGfwmVwef/vRn+N73vjflcZ7nTel9ilG/iAlpyeJVwE6wE+D2AU+xoPms+YYHbhX++UGwDTj8EygsgsGEAWy9dAuXX3YZiUwDVjqHlc1BpjFO2dUdXN72tpvYtWsHoiwFSPClL32ZT3zifyzmgcWYJWJCWjIwgJVgJORdc1vAs8E7Cv5xllN0BIALfglOHAYcGDzMoqUibTuJZSdxfQPXMPEMC0wbzKVl57P8YdDZ2UFnZythP1eCvXuf42Uve9mURx47dozjx48vxkHGOAdiQloySAH/FyTbMVrBH/4JFL8Jpd0shYbXOWMCKES6pxaJjHzgRN8wR04OkmtsocFO4aVcbPxl4p+wnGASev+l0H6ud77zN7jttndOeeS9997Ln/3Zny30AcZ4EcSEtERgGDZNK1+C65UYG/3fUH0e8aersCxUDNNhI2p2Q4KR1EqojoKzCIFgb+9pjjT3sqKrC8NOYKdS0qgbo46gUnmTyQ9OsG0mQcTrX/96Ghoapmz71re+xY9+9KN5P9IYZ0dMSEsCJoaZpKlnG5XCAcZOPIw4MCzfgWhGAswGMFPBnKScKNkXg5D6+4Y42dLH+ESBVDZDplLB971Ju5va9iHFuDDMzvvvuuuuO6PXaWRk5IwR7ZVKJRZELCBiQloS2ArGdjKZLKZbBk4h48iXL9ZfBpe+ErZsgrF++NL/d+EbYxUbLtnItisuY9W69SQTCcDCdTxcy8Oy48TdcsFv//Zvc/vtt0/Z9rGPfYyvfe1ri3REFx9iQqpbpJApr93AJeCvpjj6OJXiM4gb9PKGD/gWrF4HY1lwS+ca0T6/sCwT27ZJZ1Ikk0ksO4FpWsSB0fJCd3c33d3dU7Zdc801nD491bNq7969nDp1aiEP7aJBTEh1CQMZPb4FeBeQwnMrHP75f0Kio+WP0yeh/AS84+3QlGFRF/+RkRFGRoZJJi0amxvJNTUv3sHEWFD89m//Nh/84AenbHv3u9/Nl7/85UU6ouWNmJDqEiawFZJboXUNjP4ACo8DoyxLAcMMKA/B2H6ojCAtJYuYGevrH+Lk6X5KFZes58c1o4sIpnnmB++OO+7g6quvPmP70NAQ//W//lcKheVb251vxIRUlzCBtZBcD23tUDkIhe9xMaTqFM44FMtQGgFLJzvouKAFhclEscTYRJFyxcFxL44Lghhnx2tf+9ozpuGC9DZ94QtfYHAw7OD2PI/x8fGFPLwljZiQ6goR6WquCTOXIZkapGoVcC8iMgLIZCHfCPueEQ5KZQAfnAVsuUokG8lk23ndjW9g+/atDA5OkGsoQcfCHUOMpYPu7m6+//3vT1HlHT58mJtvvpmJiaU5BmahERNS3SANrALTlJDA78WvVvBHB6HSu9gHt+DwPKhWYf8eISTPkeF9Cwnfd/HcCoZlkUimaGzKk0qnFvYgYiwZ2LbN+vXrp2zLZrO88Y1vnJLGGxsb4+GHH44HDs6AmJDqBm3A22SsQcqHsc/gF/ooL4J/Wz2gXJLbd/5h8Y7BqU4w4RQ4cfoUq8fW8+pfeRmWFcu8Y8wePT09Z8jGf/nLX3L11VdTLC4j78kaISakukEa2CQODN4+MXKLwapm+XlsePGOof/UKU73nsT3Y0HDxQcPidFNzmdw4kyfl5UrV/LpT38axwn7GA4fPsx999130TfhxoS0qMgAWUSs0Ax0gvsMuIe4mAQMZ4MBdDSCacCJ4dC/eaExPDjI4EA/nufi+yaGEUdJyx9+5GdthSwtLS38x//4H6ds+/nPf87nPvc5KpXK5DbHcRgZWbpTn88HMSEtKt4H/A7wCDI6wkP86X5JTEjSe3T5ZkgYcOgoTHiw0HGj7/u88Pxeckmbkb5T5JtbyDTkF/goYiwOXGZrRXSh2LZtGz/72c+mbPvZz37Gr/3ar11UtaaYkBYRdq4RO7eS8mAG3xlAiOgIMRmJT0XWgLFBwICqvzgRkmEYrFu3ik2b15FIpWR8eYyLBMa0n/OHZDLJqlWrpmwbGxvj7W9/+xRCOnXq1LI2gI0JaRGR6Yb8ep++R09SHXsK+BwXS+Pri6HZgBUGPPcMFHwYXaTTYhgGb37ra3jFtdfS1NqOMUOjZIzlCIPzrRvVClu3buXv/u7vpmz77ne/yxvf+MZJY9/lhpiQFgVZ4PLg5/PAz4Ddi3pE9YZhHyoetC6yhsD3fU4MDXF0YIDt+NixqOEiwuK/19NFETt27OArX/nKlG2PPfYYH//4xxfysOYNMSEtGCzABjMNNIO3Gt/x8MqH8P0DwHHi6ChEGSj70JGBpAWNvkWp7FGpLvw5GhobZ2BsLH57Yiw6enp6uO2226Zsa25uPiOSKhQKDA8PL+CR1QY1yz88+OCDbNiwAdu2ednLXsbevXsBuOuuuzAMY/K2adOmWj3lEkMP8HLo/Ah03Q3AxNGfc/qRL+NMPIMQUowoTAN2XQ9veVuSD7x7Bbt2Lo6Y4EjvAIeO9+Et0zRJjKWNV7/61Tz11FNTbvfdd99iH9Z5oSYR0gsvvMCdd97JZz7zGW644Qbuuusu3vOe9/Dwww/zxBNP8M1vfpNrr70W4IzJjRcPLCAJTT2y0p48iu85+J6PzOuuDyWNDoEGaCIcgqGJg2OEkgvNsM9H50RLJ3SthC1XNtPeYHPyQBGzurDzJ9K5RvLNbVyxbTuXb9kyo9FmjBiLjVQqRWdn55RtO3fuPENa/vzzz/PTn/50IQ9tzqgJIe3du5f77ruPd7zjHQC8//3v5w1veAOO47B7926uv/76M8YFX5QwDOjsBLMEzz0Jfn2p6QyENrUVcBXQAqwMtrvAACEh2cH2ErXPZq25BK55rcHLX70Se8Lg4A9+SXV4YSOUprYu1m97KW97y1t5yWVbF/S5Y8S4ELz85S/n5S9/+ZRtn/3sZy8OQnrLW94y5ffnnnuOTZs28cwzz+D7Pjt37uT48ePccMMNPPDAA6xZs6YWT7tEYCAxxgpgG1QTYNZnPcJCoiJFAomCXEKyuirY1go0Bo/5UfCYxuA+B/g5FyZeLxyH/kd9MpeUWd2U5D/+Sg+jziiPHKqdc3ISOd6z9jb19cFTT8HwuLzAizW4j7Es8LrXvY4HH3xwyrZ/+qd/4vOf//wiHdGZqLmooVKp8N//+3/nQx/6EHv37mX79u184hOfoL29nQ9+8IO8973v5dvf/vZZ/75cLlMuh5bOo6OjtT7EBUQGSIPdBd4K8DugOAjmSeqRkQxkkY52X3gIsZjIh2UF4RzbluDxx5D1uh1ZsyvAfiQR6XB+ycjSKAwegfLJErbhs7krRVvD7D+uRnC82tao0yvSTMpLyAb39UWOMYG8M8NAulggXz6FNTAMwxPQlqUelFcxYpwP1q5dy9q1a6ds6+/v5/vf//6UbcPDwwwNDS3koU3C8GssaP+93/s9vvvd7/LYY4+RSCSm3Hf48GE2bNjA0NAQjY2NM/79Rz/6Ue69995aHtIi4vWQ2A49b4fCAIwdAffT4B8At/6GeNlI5KOLdyNSQ2pEJi60AHlkIW9DiCmFRFWJ4P8JJOL4DEJUR4Bx5h4tmQYkLLjr5SY7euDajSZ/+e8en3z4xenNCo5jfXC8zQhxZoCXB69lRXDcVeAfEVWfAVwS/P+/AbswuNmweNNv3EP3rpfB+94MiViYGmP5oFwun2Hy+hd/8Rf86Z/+ac2fa2Rk5KzrvqKm365//dd/5TOf+QyPPPLIGWQEIk/0PI/e3t6zHtiHP/xh7r777snfR0dHWb16dS0PcwHRCX4XVFyoHgP3UXBPgV9/ZGQRZqSUkEzCaMJDIh+9j2B7GlnY0whRJZFF/mXARqTmVAHGgO8jkZSNEIVGJzNdESV9yLvQlvGwgR8/4TPa67MG6OXcBKeEZEVubcFtLdAJ6CeqAlyHRHIgkZ8DvBtYZ6a5zGoge/AIZJvg8CtDQkomRY/emljc+eoxYlwAUqkUqdTUkSqveMUreP/73z9l26OPPsqTTz4578dTM0I6cOAAt99+O5/+9KfZtm0bAHfffTfXXHPNpNjhsccewzTNcxLMTCdoacIAusDtgrE+qD4JzoNIvFB/sJFFHMJ6UQIhmDRCSAVCwYMb3J9FyCVHGCkBvB4hDS299CGOfSWgASEGCxgkJIMo8ohTw6Yug4wJn/0/Po4L24Ahzk1IGq3pgFkPSSeuIySkVUB/cAxv5Mzy0CsBrBykeuD5F6DswO5TkAgIqLERmlLQkghPWowYywBveMMbeMMb3jBl20c+8hGeeuqpKdvmwy2iJoRULBZ5y1vewi233MLNN988ObL3iiuu4CMf+Qjd3d04jsNdd93Fu9/9brLZbC2eto6RBhrBaADDg9K/g/csUlWpT3t5JZoUQkJJhBQyCIEE1TBaEPJpDf7fhKTEssHjvOC2jjDCMpBI5P8fPFcq2Nco8EDkeT2kdvOvkf2sTKdZ3QS331Dkn16Abx8WYjwXigjJNQXH2ol80MvBfRNIxJYjjNRmhDMCXgnKJjgn4TOfhEQSbFsIaeNq2HAHpKy4xTzGssadd97Jq1/96inbPvWpT/GP//iPNX2emnyN/uVf/oW9e/eyd+9ePvvZz05uP3jwIM8++yw333wz+XyeW2+9dck2bM0OJpIYyiPLdRE4Ae4+4DQzxwL1ATXZV/GCkpKSh940YspGfo+SmBPsJ8PUqMsHrieMvECio6uC/6cRqu4DnkLqVs1Aw6hBS8pg69oEPxpwqRz2ZiUH8YPnSRMSa1Sw4UVe59l3UgW3Kgc25sDuJ0NCamiA0hAcPQnZBCQN2Z6wobEhTuPFWFbYtGnTGaYGP/vZzyYNEKJwXZeDBw9Omfc0W9Rc1FBrjI6O0tTU9OIPrAu0IOMkWpF44S+BvciKpsmj+oQuzh3IAt5IGA11EdZiOpDIYxWy2GeQNJhGSBZTIw5dlv2zbKtM23YS+OvgeduBN9vQtTpJ+gM9/N13h/nad0f4IRJdnQ16/K9A3olOYBNSH1KhQwuhCm/WMCJmmwbQ1g2v+TXI56AhA90dsH4VvO2NosqIEWMZo1wuz0g6AwMDXHvttRw/PtV9ZsFFDRc3msFcCY2XQ8WHwjCy3FbO/Wd1AlWhaeRjI9FOBYnztE6kj2kiTOPlkChJyWimpfhs26ZXC1uAXwmOJwc0OWANuTgPjdJwqMxKhPhKnP3MVpC03hChlH082DYW2ZbjRSKk6fAjFxQ+MD4Cux+FVApSCUnjndgMOy6FTBpSScgmpe6USsdRU4xlhXPV+9///vdPeumVy2U+8YlPzGqfMSHVDO1grYWOnTB6EgonqOeIaDo0DZdjKiGVkMVc6zH6mGZCgspTuw9SHnjTtG3usEvhn4doQNRxLQi5nI2QysGxn0LEDwYSJeUQklKhRZIwfQjnoUsojsPuSOe7mYBjl8O1r4S2FmhqgM4myGWFkGLEuAiQy+X4yEc+Mvn76OhoTEgLj6sh8TKMS9rxjx2BU/t48fL74kNrOp0IuVQJpd5NSDTSjRCQ+tolETJoRypm821gYCJE+FJgM3AZ8ATwMc5O+R5wCBhBiKYFCWoMwnclGTwuR41Ecp4Dxw/CZz8Oba3Q1AQdPXDpVrj9P9TiGWLEWNaICelCYTaDvRrYBIkuqD4HzvNIS+hCD9yeOyyEaPJIZDRGWO2KihoSyIdF6y4eYa+SRiE286N+Vhl6E1IbInj+S5AzXCUYVxEcv9a7tBdJe6iKhEq78eCmj1NfPn2+84MPhQnY9yycahLhQ1sfWAmYKEoKL26sjRHjrIi/HReK7I3Q8XFgEJyD+N9/I7ijnL9pzsJCpdw9CPmoKF0bWDWlZQf3RxfuEhKBFIL9tC/A8RpI2q4R+BvgKNIo+zxwEPg3QlHD5YT9SPrTQkjpGEEZiLBG1lqLAyxX4MgJOSrDgGQGxovwpptg/WrobKvFs8SIsSwRE9J5Q8x1jFwLxpoWvENPw+husQTyl4aQQZ0WtLZiEcq2NQpSSjUjj9eenhJCWAtFvdHIJYMo5loRR4hLgV8C+wgjpH6EiHJIPWkUeW0tSBpSo6UKNe4O84N/fB+qZThxFL75IGzcAD09sHadpPO62mOhQ4wYEcSEdN4wgRaMXBP2mhzOCy/gjTxNPfcaRWEQOhpkkJ4gn7BXRwlJH6uy8Cyy2GuaLNpntJBIIZFS1POjG4mQJhCyHEKO2UciOXX2Xo3UzMrI61JHiXmB68DJEwEhbYaVq+G6V8G6NUJIWtiKESNGTEjnjwRwBZn0etraoS/5M4r8Gxc2dGHh4COLcx8SOeQIo51mJPJYjYgWViGLfQeS2tPZSCp+0KhpsbEV+BRCNBUklfcU8AVC1+9qcJ9NaBbrIq87G9zOdGG8QIyPw7PPwoFDUkf68YPwmjfCZX81f4W3GDGWIOphHVlC0CpEB5gd2O1XYKYyeH0/wS8fRoxvlhZMJNpoQBZiFTNkEJJqmOFmE0YU0frMYiOHqPCqCMG0IaS7jtAUVqXqw4QOE0pg8xYleR4UinIzgMppWPMsPL8b8llIp6AxD3YSEpn5OooYMeoeMSHNCQnkmvodmOlraX7jm6kc/XuO/e0NU5smlwhsZBrsZiRacBFtYBPyKrUZVtV2ejvbh6YeSAlCNeAaQqcJJRwDqTN9H4n6Fnz594EhH372FHzsP8P27bB6DVz7cmhdAR2bF/qIYsSoG8SENCfkgS2Q3YSfW8HEkUO4p3vBr0/D1OnQyMdCUlOXIcq4DkRtViKccxSVTmeDbRmmjp+oV0SPL4eIH6ICjWzw/zZCl3L1u5uTldCFYGwcnntBjnZ4BJob5UBjQopxESMmpDmhAdgI2TX4uQ6KR5+Hsd7FPqhZQVNreYSYWoFrCGcY7UfSXC1M7c1RQlJLoQVbsGuEDBIFRpEntCzSJKw6VCwYCiU4dFw878bHRX2XbRMRhCrvDAMfY/L3er8QiBHjQhET0pzQDFwJbWuhKQO7vwblXy72Qb0oDEJS0WgnR1jMtwiJykVSWT2ImKEFMSZVm53lsCg2IXOVFDqqfYIFFGhUkS7evSfhRAFe8UoYPgW7HxJJeCYDmQZI5iFdkw6pGDHqHjEhzQo2sA3M7ZDogephmHgWKvvBPb3YB3dOaASQJqwBJYNtBcJheypu8JGm0laEpHQW0nQX76UMizNrR2oku6CE6wElB6winDgJdgKsJHS0i8tDcyvkHUjkxCfPWC7vQIwYMyMmpBeFgSzNfyrTQ9tTcPL3ofAdFr77Zu7QOUVqjpondFo4RhgxtRCSVnNwywf3XwwfkplIasFQKsO//AC6umDDZli5ClqaYXUPrF8HuYxESVZs0BpjeeNiWGsuECuBtWB1gWfC0EGoqONb/UObXEHIpQtJvSWQor7WiVTEoMSVRqKjbGQ/yxmL+vo8D06PQdUCPwmn+oWQejowJgpw4gSsSEJOB2cs93cjxsWKmJBeFJ3ABrCaxBaofJKlYJqq0DHimpJrIpz02kqomtOlrpFwvpEKGWLMFkbkp16wzOLCxQdGi4EuPbh8aG6C170ayiUYHoJOFanPaYJTjBhLCjEhvSjWADugEjXTWTpQrzqdWVQgGA2OkI8akWqtSCfC6gC++R4tsbzQDEbQ3OpUwCsjBkaz/NwUSnDsZPj/wUFY0QXrVkNG35EYMZYvYkI6Azo9pxHRmm1DEl0/QRaX/dSbI4N6zSmq0+7TtJ2OlVChg/YlNSAklWVqhLSchAzzi6B12G4EIwGmDcZ5OOT5PjjB35QqcPwkdHbA2ATkHEh6xJcIMZYzYkI6AxbSMroDeDuyRJeAWxHP6PpDAiEVEMIZ4cxEkTttm0k4jjyPNMgqIakSL8Zs0Qy0Q6ZRTqxbALcM7vD577JchkefBMeH5nZoaJFx6JNVvRgxlh9iQjoDFjKbdCsSKR1FDHXqd6SEg/TQgJCOGkhH60cg6bu24L4KQrPqTa7ElA7+Ji6bzwUuUIVSNWxqdXXwhYrr54iqA/sPQcmAAWDCgks2ws6dYMVRUozliZiQpiCHXO1eglhy5pDVQH0M6hMeYhAahRKSklLUBgjCvhsveEyacDLs0icjTVQuVM3PBSpQrQAmmD746oNe4rxsWz0P+gagaMNQCjZslGF/O3bEhBRj2SImpCn4IPAbQCNYBqSqUNkLzkOcueTXL1TjpaPFk4h4PY3MPWokHLKXRiplaqK6PNCIvLJ+5tHDO4Jx5GxOAAnwEsg7oOJ5HaB+Hig50DcBA8PieecvjXaDGDHOBzEhRWG3grkKKn3gnwTv5+DvRmpHS8NAFcLeojSwFiGclyC1pV5keTQJJ6YWkOt4JS9YilFSdLCQ+n03EiYwK0hcOB8XFh7h3Fw/eG6NztQBsBK5fy67LkNlCPY9DzkL3CL4hognYsRYZogJKYp0ClIZGDoA3r9D6U8X+4jmDANZApsRT7rrgS3A64CfAl9DLNQqiHhhEDgR/L+R0Fx16SWFol7ddvD/lYSvZgiJYPqpfSovasCkEpMSUAUzDZTBq8jPuaZ+vXG5fa8fjh6EP34PpPV5YsRYXogJKYokspaMsBRbjsgixNKK9BOtQ0gpDfwQWYrXIYSk1+llZGJsGVlC+wlnIS0taCWsAYxGMHPQ1gGJpHjElUehMgpDPwG/1gIVPZtVJp0Ck3mwDXH18Fzk7F7Ih6oEo33w99+ArRth2yZoWAG2EvHSi2ljxJiOmJCi0FyXUWGpjCJXqBNDFuHUPGFzqwEcIjROrRLWl1T4EJTlKROKH5bWMqfdVVkw8tIT1NgOmTSk0zCSg4kMDKeDYYq1Fqn4wT6D/dppGVfulcGxwbvQM+lCaVyk4F4JWm1Y0QAZU0hp6bxRMWKcFTEhnQEXeBp4brEPZNawCXuKlIBKQB+SjisF24tI0ipLqEPbALwKOInUksaRJdVFJOJLJzFUZnK4esqCTApac+IJt2IF/PJ5mBhH5Px9iLVsldp6EqpwYQKSTdDQBo2NUByF01bwvGPnv/uRUfjy38HPNsBPtsFtb4JNm2D9tWAsvSRrjBjTERMSiHFq5jXgDsHol8B7DDi42Ec1a5iE84q0egJCLBoNmcF9KcKLaRchoKPIUllFYozpTbT1D4PJXiAdVm74crNNyCSho1Xk2AkHinkoJKF4DJzxGh6HH94MEwxb6pKGCbkJKI+BcwGE5PtQLMF4CUbKUKxCZemIbWLEeDHEhASQuAQ6/wf0/wEM/D+LfTRzRnTekc46ii7R1ch9SUI9WBmJoFS0nESc+3yWkqhBYz3trArEBHiAG5ycJGxYAas6oKcVBkfg6Eo4VZgnQgqOy7Ag2wTJrKTsBk6D03fhT+OZUE1CxYKqXn6omjBGjKWLmJAAnDIMH4dKLRen+YeJuOypH10KiZK0ftRFSEBNhK4NUQG0Sr41NecSzk9aGqSkJKCv3oKGDLTmYdNq8YJb1ynecFUHulrEG25lN/zoFEzYSIWtFiqWRnGFz62FXCukM9LgWpqAvmNBNKNyEXUWLDPnloKTJ6BYgFtfEexjnPCSJEaMpYuaeWfeddddGIYxedu0aRMAu3fvZteuXbS0tHDPPffg12Njn1eFUj8459m8uEgwkIbWLFNTdilCktIUXipy023qX+4QOjoQ/Eyx1IxVNVLyA+dYExqykM9CLg2NOWhuhM426GqHnnbItIHZQu1eqSVu37ZOeDXA88H1xMXBT4DRAIa+a2rUNEcUC3D6JAyPwvgEVCfAPc8+pxgx6gg1W3OeeOIJvvnNbzI0NMTQ0BBPPfUU5XKZt771rbz0pS/l8ccfZ8+ePXzxi1+s1VPWDt4YFJ6sTTplEaDX2QWkrK4uDaMIYXUgU51akGWwGjw+G2zrBjYhkvBoDWppoYRECoMwcRoGT8LIMIyOS70lnYOODmhqglwWLBvyOWjKC4nUJB4cCeqQIzA4DH1DUPTAzEL3Rsivh+Q6SK4GuwcR6J+nP4bvw0NPwXcehmP7YExlKUuwXyFGjAA1WXscx2H37t1cf/31NDc309zcTD6f59vf/jYjIyN8/OMfZ+PGjdx33318/vOfr8VT1ggGslTbwL8Dxxf3cOYIH1mG1W1Ba0MlpA30MLAPSUjlEPLpQJZAM9jWiKTntPeoFYmufEInh3Hq2Vp2OjxwSiIgKIzD2KiQQ7kazN4IYkMfaMxDWztYHdSm88oH3wF3HCoTEslUPSAB+VZo7oCWLmhbBfl2pgrvzwMpU5pkEyb4JSgPgle/nosxYrwYalJDeuaZZ/B9n507d3L8+HFuuOEGHnjgAZ5++mmuueYaslmx9NyxYwd79uw5577K5TLlcmjvMjo6WotDPAsMxFynAnx9Hp9nfuAjZGETmqcmCUUKo4i4eQVwC7LkJhDv8jJhn5KOncgQDu+DUB6gj9U6U32Xzg2oFqE0AuMj0hhrBvLvbA4hIzPQtbeCW4Vjq0Uo4A1f4HMHsak/AhVPyKjqQzIpkVg2L89np2GoF4aOIu+czXlRflMaWjOQtcEoQqkMVhuYcS0pxtJETQhp7969bN++nU984hO0t7fzwQ9+kPe+971s27aN9evXTz7OMAwsy2JoaIiWlpYZ93X//fdz77331uKwZgEPeJalmHfX62qVauu01xZEzAASMQ0E//9WcF8rYkRhI7GhRkaNwX4qyPjB4eDv1d8u0KzVudAhAbRB8wpo6RC3BDOQdZzqh/GypOlKJbAtUcFZSejuhtEEDKfAO0o4zGOuyCDUvQKau6GxA1ZtEpVf1ggacoFMFhpboVyEkX4ojUL1GBLnzvK5fR++9X3Y/Sz4Llx5JbzkSqldxYixRFETQrr99tu5/fbbJ3//5Cc/yYYNG7j00ktJpaZeraXTaQqFwlkJ6cMf/jB333335O+jo6OsXr26Fod5FlxAX8giQkUI0flHOr5Nz6yDRDklpPFV9VzaSJtHoqFccFOpuBv8rdK0F7nVNyEZQEKcC5JBncgMZuUWyxIF2QlwfbBTAcu6kLDBThIaoV7I89tAFhKNkG2BXLM06WaM4HlscY4wgMYueXMqFvIOzTH2PHEKyhV4+nnoXANbfXkjY8RYopgX2XdzczOe59Hd3c3u3bun3Dc2NkYyefZCbiqVOoPEYpwJm7Ac7iHpObUDyiJpu15kedWx5IGPAR2IC4Naj+rfKFFp9DWBkNhI8NOK3OoTLjABlSIUK5Dww0+4VwXHgrIFiTQ0NkPhaejrg8FeKPaDd5zzHhMBSISTASzwbHk+H5lj1NkNK7qguUlI8Ogh6O2DUQ8KFTCGpA40V4yMwYP/KgMBs+3w8ldC8/IZJBLj4kJNRA133303X/va1yZ/f+yxxzBNk8svv5xHHnlkcvuhQ4col8u0trbW4mkvaqhvXZqQaEYRc9R+hJBUCA1hhKORj4oftIV0AllOtVfJIJSPN0aep37rRxFxgGoFbBuSCUmZpZJSy2nIQkMe8s2waj2svxTWXAIrNkP7Jkhc6Ihw9bTzwl8xhQQzGemR6sxCdzOsWAWdXdDSHogtzgOeK0rCw0fgyWegULjA448RY/FQkwhp586dfOQjH6G7uxvHcbjrrrt497vfzete9zpGRkb40pe+xB133MGf//mfc+ONN2LFEy8vGCkk3ab+BBVEWeciVYzpQmYloyphSm4CWS9NJHGppqpKOtrXFL3erl9CisRuJjJgMREQUjYNVjqIjBoknZfIwbpLpJbT0CQuCqkmKJ2A6oUKaZT2jcBU1ZQUYSoFuZQU8vwWWL0OJibELPWUGrDOdV6SLyR08Ahkn4B3vOMCjz1GjMVDTQjpjjvuYO/evdx8883k83luvfVW7rvvPmzb5oEHHuCd73wn99xzD67r8tBDD9XiKS9aRJ0YckgkpNZBZYSU+gkdv9W/AMJr92JwXw8SASUIzVVPMXX0uU2ouqtfMgqqX4kUNG6AVRugoxtKHjQY0NECmRZI5aGxWwbcVRwhIyMJg0GvUjkpdabzPobNkOiG1q0irMi3g5+BkglDE9AQ2Nwmk5BuhJ2XQTYBbY3w3BZwjiGJ1vNASx7Wr4BULGqIsXRRsxrS/fffz/3333/G9ltuuYV9+/bx+OOPc+2119LR0VGrp7wooUaqUbcFmBoNRSw+J+OGKJnkEXm3zk+yCJtrncjfJIL7A7F0HRMSTEYWRiK8+QmwMtIDlG2GZIOkzZxAkk2gtEtmwUoJGfnn+yqD0YhGFsyMSK+NBPiWRD4VD8qu3Cq+CC5yWYnkJkdhXMAZrpRhfEwcIVwHTEucImLEWEJYEC+7lStXsnLlyoV4qmUPtQHSSMeM3NYz1S4IwpSbHbm9AnFliMYCJaQGNRY8XmtUWYSQdB/1iYCMqmXo7xNrngkLduyClo2w7iqEfEx5YSVXxA0jvTA0DLlGSNeidjQhbt5DgXzbT4tDhA6U9asyHLCYBtMBtwR7HoNfPAzVXyIx6nlizx44dADe9RtijZRrvsDXEyPGwqN+15gYM0LTbi2EqTQ1SlWxQpqw2bWE1JfU5cxERk1oKk9nIPUH+6hOe6yBkFSa0C9Pt9ff9Xew8pfKMFaAvkFo7RAC8lyJGKyUSMHzNrQ1Sz/Ps7vh1D4YfBqq59sG4AOj4KWgOgyVHJSzUCoGswMNyPgysK8tJ4+vVsEyJYV3ofB8iYz2PScOFDt2xRFSjCWHmJCWGPRiOwe0B78XkdqR3pdGUnI9iCtDiXDGkYFUKbxgHwNI3QjCmataVldV3ShCemlC+Xj9ylI8qFRgvAB9A9AxIrJq1wukg0kxXm0MakueD3290Lcfhn+BnK3zxTiQAncEqk1QboBiURy//SrYVTHwvWQ1YMhxWjak0oHK7jxEDQrlnoMvQGsr7LjqAl5HjBiLg5iQlhgagcsRYkgg0Y6DvJFaN2pG7IIuJSQdJaYCMg9Xx1VECc5AoqQcUmdKB/eNIaRnI/1L2lRbX4h4TlSq4A6KyWl/Pxw6LLWjdFqih4wpJ7IhA91t0HcN2GPw/CNIrHg+Q+8MeW6rEbIpMIfEicFvFKsix5c3YKwCe/YCDhQH4fhxGBkHL0PYynwe6GyBdV1w9Q7YekkcHcVYkogJaQlA22ogFBxo6k47XlTsoFGMDjZQy59oH9IIksZTQYPWmXRWko5BJ/I8mhLU5FL9EZKemaCiZljQ0CBS60pFyMg0IGEESg1DCv/pJKzoFqshoxH8oQs4Bn2XSjKd1jbAKMvkWIOAmIBTveK3N3oK+gdgvBiIKS6ARDxfhBqlsrzeGDGWIGJCWgLQaa8OQi6nCIUGmoazCB0YdDDfKcTte/pQjWJkXwlCU1ZNw2mPU5T4BhEiKxGOs6g/BFrBZCNkO2DnlTJywgrIqTkP7YaEkGr4V7FhywbYtwGSa6B6Grzz8bILjFXdcSgcgfZV8nzmMBhV6XEyDVH47X0BBk7BoeelhjVZubsAnByCvlH4/k9gzIFLr67nvGqMGDMiJqQlACUcbVRVRwUlI72uHiOUeqsAoYxEPDnCCEn3l0L6mZoRM1WdHDse7GcHIo4IyvWUI/sYDfZbPyY1RSZjRceCsgEjA0ICXrDYV8uQT4CVgA5LTthQBR79Oex7Apxnz5OMFIHZkleCMQOcCnQ3Ie9aVWTovgmZBkiPEVb0hoK/rV7AcyOv89nnoaEZJlxIG1IvixFjiSAmpCUAJR1Nsakzg4mk5rQUXiAcF6FpuEzwmDbCCkWZ0Iy1ERnQNxLcr8q7UvB3jYQTZXW5dAnHXtQPIUVGgbsWVAwYHUTGUTgSJXketLZDgymPGfXhdAWe3g1H9oB76AKPIfDB88ehYIi6r2M9ciarMqvIsMTtO5URtZ83AP6JC3xehQ/HeqGzF8ZUwRcTUoylg5iQlgAqCOFkCWcWae9RJnhMFYmCVM4dtf/JAmuQ5XIYKdtrys4K/m4jQkLDhFLxx5EPSDF4nJq0puflVdYCDkKVJfCG4PRRGfGdyUvNpjAGGzfARAqO+jDswZALJSdolK0VfKAf/DEYXgtONahflWUWkp+Rk59KQTkNbpoLU/cFMC14652w8VL4yZNw+Tq4dD6d8mPEqC1iQqpjqJhBI6BJz1CmmqCqdVATQkg6RsIJfk9H9qelf11+tUakgxd0CHbQ5on6CKgqr357kBQek/FjuQhmSUigUoVyWQgqaYivXf8EDAyLJHy+UC6DVYSJAlgOGAVJ3RVGCN8FPaM1mMvl2WKHlE9DKv56x1haiD+xdQwdM64iZCUhCIUOKtNeS2iqWkaIZQjx8cwH/9flT/uLNP03gkRdOn7CJvTGMxAy0rlJ2nw73Y6o7uB70gNk6QhDS07k0aMwnIfhFjiwHwb6wZmPsd9Z8PMwUYHKuIgnKoGtjzMRiBl8Ga5XK3g+/PJ5yDbBf7oJ7DhdF2NpISakOkUmuHUiSagKQixJZF3Veo7WdgYJjVarhLWkE0jKLk+Y5lNJuI4t15qQXp/rbKRq8Phs8LxqH2QQyshd6tTnzvdg7HhAAgC+DLNra4PTp+GRk3DiMIz2Q+E5eexsdhv8VHI/+2sP2pGdfeClwc2DlwTPEmEDRWAA/GHkTNcgOvJ9eOEINHXCqQloyUBDbLYaY+kgJqQ6RRqJSpqRBa+IEIPJ1Imu0eZVRdSqc4jQ807l3RkkwmoMtutsJAjrUhqZGYRD+zSq0mPQW31eh3sydK8K+A0yKdYHxsag7zQ8/ST0H5fH8HPCs/bi0AbkcyO4LPAKwbA+NXNSh8BR4Nis9jQnnDolwoa+CUnZxYQUYwkhJqQ6hIlMc00ghNICrCK0/wGJfrS5VZ26NWqBUCYe7THSgXvqwqAKugqhIKIpeL5mQuIrBI+pEpJiOfjpBI+tv5aXQHfojMPYiNRVSj6kD8gMojIiz6bAXEihStg8PHsidpBLhkzwXGOE06hqCR9K+2EwAc88DclLoXNdjZ8jRoz5Q0xIdQgVFCQIoxCt22jJHkKBgxKPihE01QZhuVwFEermkA7+RolL1XN5wrEUHuECrP1LUU88TTRVIvvS56wPmEFY50qkUjWgUJTm1HQWxqPTouaOub1OH4lDp8eks/vL6P/PKSrpWQGrV0F3iwwDjBFjCSEmpDqEDxxECKILIScPcUjwkHpPmjDiUbsggvtHCUksChUtaISkYyZ01IT2JekSrUP6VMAQJcoGJMgoEZKcuo/XB4Jko52EnAsMgTsBxVWQTENnB4z3QWkimNQ6+72ev6BjJLidPzRFOmNEappw7Wvh6qvhtVfJ7zFiLCHEhFSn0EVPIxQTES5ER4+DvIE5RPyQREjoAGH9J0c4ikJdGdRhQdN0ahWkxKKOENqrpOMrYGqlRcURIwhJFgiFEgqfMJ0Y9cibf3hAHzijUBiWZ7dy4lU3XoKREzB+JPCum1nppuf4YPCIjUz1FVxI6HtRICRFFZ9MwgeOnxDDVp/YYDXGkkNMSHUKFRFoHadK2CNUJTRWNQgjljSyDumAPq0baS9SmlDWPb3GpMmr6CA/NW6tRP7vTNu3jdS5IOx7UqNWCFN6OhZjYZfIcfCLUCkiTtw+OCNQHIKB5xCXv+JZ/1pfrzqqr2dqb9hCQElRL0wmCFO1ZzYo+yJjHxgUCbjm92LEWCKICalOoS7cUWJQ0oiSyniwbRRJn6kCTCfLRmcXuUi0AqFKTyfMqhWQ3mcijg7jiHQ82rqZAYKJPpMpwjJh5DaGzGLSqkkheO7GWpyYOcNFSCcnPUCHnhEHBU7wYoamJ5HoaIKwL2uhJ+fqeJCThFIIVV+ecfS+L1Nj2ztg3A9D4BgxlghiQqoz6BW4Xp1XCYmohfANq0Qeo5ZAOhOpEvxfp8dqCk2bWjWdp5GWWggpmamQQo/HJlTZNRJ66ilJJSM/9Vh1XIWm+FSZp1HZwkKpEXALyBl78YZUm2DiefC7ttgu9JdGib9KKOG3kfciev4xDNh2GWzbLqMv4hJSjCWGmJDqDBoV6ZRXm7CxdS2ymFeA08gVcwWJYqYvryr11qtpJQh32mPVqDX6QVDSUkuixuD5xpHBfype0HSSiiKiIyv6CCM1baZVMYY+18Jmk8aY2q314sghr1drYCVCklooRNODKirRxucs8j61BvcZpgXveCfsukoMZGNCirHEEBNSHUKvenWMRBVZzEcI6waqiouW5NVNYTWwCXgZQmQu8CjhwqbKOf2bBsQNXKXjWvPRWlQrocErhFGZ1ooSTFXjGQgJaglD5y/VuutmvqH+fiOE0VFU5r6QhKrRsL43UWf2BGGtkPEhGB9ewCOLEaN2iAmpzqAjHVRJpa2UGgnpQqTpMRURR2tHjUhj7Y5gPxPBYzU6UqJQsUMWiQYsQhWXHovOV8oFx6BEpmPTzcjPaH+MCiwgXETPJpeeiajqoRavfVsZQsEHnHm80d9rfdz6njmRm/6u5rfqxp7CF0PZ8tmFGjFi1DNiQqoz9CARTlSp1gsMINNf1U1hHRK5pAgJRlV4Y0gR/DnCwXwNSJ1Jp8e2AlcgZBRM66FMKGhQQYSiEyG6qCxce500kjgbZtufFDWRrQcUCQcRRms200lHHTLm48s0iryXvcj7qqlXA/kcOMH9XQS9aJUKVCtLLxyNEYOYkOoOKu1VcYMq46qRbSrxni4Q0J6hCqFTWlPwN7o+RRs7tUBuMrXXSB+jtR9dgKOqvegH52xkZER+ziaKWIyoKGrFNEFIPnreo31XUdcLCKcv6fuSpXZkqvU+nVGl74+eS03hucExJ1asgnXrYed22Li2PkLMGDHmiJiQ6gyjwc8OQqIYRxZHdWboRmpDKiZQwtHie5kwompl6jwkHW1uILJuXXRVeacKOq0Lac9RtNn1fHCu9TFa21osjANHkQg1Q3gRoOczSkzR3qBThOlTjRRrwQVqATWBNETr+6PvdbS/qxXIbL8M3nwzvPlG6OqswRHEiLHwiAmpzqBjyPVqWwUBavujvUcNhGJmjYy0HpSfdksG+1WyaQ72pQKGIlP7lVRMcTFdZDcCG5C+o3FCG6VWQlXbScLosIEwbaaEBHJOa0FKPmHK8HRkfyXCWpJGSgUgaVmQTMbuDDGWNGJCqjNoE2x07k50KJ46bEedGjSy0WhGh+npGAkVNEAoekgSChY0RReNiKLmrLWEvpaolNmP/B/CVOF8Yab0oUaBRUQokiO8CNDzE5XHK/GoJ6CmTi+0dKPnpxocxxChE4ZGRdH3XlWYrhkTUv1jJpvc6fed0zp32SMmpDqBNqCCLH6dyCI3jNSCRoNtKjhQ94M1hIq6J5AUUgaJgnoQ8UMOETNkCIUJBPvQq/vp9aL5QiXyvAZhOkytcWyk92ehv5LjyLkzkHOUQyKinwGvQ0QDELqra71NhQ61hBLRD4DjwCEkbacuGyqiuILIOcxkoKUF7PgrXd/QLj9tHkgytc1dzbcuTsSf3jqBXhlrmk1Taf2EDt09hP1H2neiC3kFiYzc4LGa3tPFq5VQwhx90zUFtVD+bFqs13lKScK+JsUEYU8V83BcuiQodFloIjyX2gw8gijcXMLGX3XS0NejEYuKTHJcmCPFAEJEh5ELEpV4TxCmZnPIjKz1Tc20/MrryFx3PWxaD6nYK6g+ERkk42tlMtjmlWSUve8EKYqFujysP9SMkL74xS9y5513nrH9C1/4Ak888QSf/OQnJ7dt3LiR/fv31+qplw1cZFHsIfQVOA5cikQ63YRCB51ZVCJsms0TpuiUkCrIYtvD1I+4etLB/KTlFNP3rYSkPU2NCBlpo61B+Fpmuk6ca0pspqRIdBqRRqY6xqMS3Kc2TeNIXWkEIYDo/nRZKQZ/kyN0d8gzd0cKPb6TwAvBzSNsgB0Nbu1II/OlwKXtHXT957sxN66BtT2zfKYYCw8X/PK0/wc+/m4BPAdcFxIWWOpnMp8dbvWJmhHSO9/5Tm655ZbJ38fHx3nJS17C9ddfzwMPPMA3v/lNrr32WgAsq146TeoPp5Cr4iphE6s2y+q1ry7oDkJSFnI1r5FRM2F6rgNZJGf6OOtsnfn4qIfNmmc+p6bm9FOghrFjhA4RJUJimE5Mmq6MQp0toma00//OR1JhSkLPB/t6OWHNLEVYm+kBdiIR0jAhiacRa9ZR5IJB3SquJIyizueclhDiO4D0kD1H2FDci0RIqrbTGmDCsqG5TQYOxqhjBJVIP5CluA44DrhVcIJRlwl1gRwj1FQmWCwHyMVAzQgpmUySTIZLwKc+9Sl+9Vd/lTVr1rB7926uv/56Ghrqa4RbPaJE2Pyo0BSTLkaahtNFz0Wu5NVtQf3wVL59rjd5PvonVbLsM5UUyggBjCCkaSOLuoMs9Eo0SlrB0IjJ1xpVmilha2rPj9ymny8lXU2vKdGXI/+PNgZD2Ai7EiEDjeL0nI4i6dRjhBFpkVAqPpfzqmrJYYTgjgU/BwgvGtRRXc9jGTBMG8tOYqQyce2o7qGfRMSV3fPBc4WYPP2kQqh91Q5BldREv/GwXCOmefkUl0ol/uqv/opHH32UZ555Bt/32blzJ8ePH+eGG27ggQceYM2aNTP+bblcplwuT/4+Ojo64+OWM6YvZtqtryQTVcydIhzA18LUlFiBsMk2x5mYrzhVJcvJ4NhAFur9yOJ+AHglkp78ISK3fgmysCtpQTg4wovsR6OcMWTB3oyksCBMobmR/2tNB8LI4jjwC+BaJA1nBsf1PEIsWSTyXIlESUcR8tGIL0uoxusnlNufCI4vT3htO5svWBXYi0REP0Z8B48RGtTCmRcoAIlsO8l8N+RyorCLUccIdKyGkk8JTFtc2afImZSQtPHDIBT5awVz+brmzssr+8pXvsI111zDunXr2Lt3L9u3b+erX/0qe/bsIZFI8N73vvesf3v//ffT1NQ0eVu9evV8HGJd4WwBucqKtdfkFEJMpwhtZCYIr8x1pLhezU9E7p8uMp3uOnAmDOjZAV2XM3XE34tfmakMXV+T1rm0x8pGFtx9hKKBZ4NjVLm6OpqrvJngdQwBRxAyWsNUOx/9auv51NpO9LjU4cJHiL4vsr9gxuxklNIXHHcXUsPrCY7reaSu1Bvs1yGsJelwC60QuJHbTFGTRru63GSRGtRapsrzCe7vBl6ez3Lnuh7W/catZN71q9CUguTyXaSWB4zgQ2qCYYGVAjsJtv5MhI/zjeBNnx7fL38Yvu/X/JVeffXV3HvvvbzxjW88477Dhw+zYcMGhoaGaGw8c2TbTBHSciclvYKfmLZd00ba66LwCb3lGpAr8i7kar8l2K6Lcg8SiWgkMOtA37Bg153gevDUg0E+zGCqv/jM0BSUqugGEQI9HPwcZiqJ5oJjfBWiBhwNHnsIcSxXz74hhDj+PXjNv8bMpKpR4mHk3E2XkR8GHkHOTS54Pi9yXxW4BEkrthKmyrLAk8Hf/jJ4jXnCZeMlyPuwBXl/WgmdNGDm/i518H4OicR2I1HXoAEPB+duMPiGpoHtwG+s7uR3tqyF//7/g8t3gpGN+4/qHtOthkuitvM9qSV5HrhlSb0mEmCk5Ds4pQqretiFnFl84RgdHaWpqYmRkZEZ1/woap6y279/P/v37+fGG2+c8f7m5mY8z6O3t3fGg0ulUqQuMunq2byZNTJqRVRVJcLaihbutyKLntoBRX3wDISw5p7MscHMwGU7oKENOlfA8DCMjMD+n0JlEFkqzw7tldLx305wLJpCGyeUo2uabi+yaEfraHuRiEnTcprGU/HCTE20o8Fjupk58swi0ZXK6qPndRwhpOcIDWzzwbEOI1HRweDvdMChXgQ0I8SaCo5hnLBWZgavQ2PNaD1snDAluB3o3AjJNritHb55FD75C3mshXwOcq1J2NoIOSNIAcWof2h1c4brfzsZSMERybehMqaZpm8tHSI6H9SckL72ta/xlre8hURCloK7776ba665hne84x0APPbYY5imueyjnrngbIO0tQgPYb0n2sejA+O0j0ZrJ0S2R/92TjAMyDdAexcYaRgchKEhGDoJowmYODchaeQSdZjIB8cUzYJnCQfPjTBVEEGwTU1g1V8vmsJLc/aBeVEXcs3Cax0uFTynnsfo0DsIa3VDhDZLfQhx9COEo4IDHZSnYyF0uq66OSghVQkHFGqNS1WVQ8gpT5vQ2ApNPdDYDpsmYEMKKlXIeBLVNWZS0NYYXEnHqbqlhenpOMAIksiWL2RkaPUxOmHs4kDNCek73/nOlH6knTt38pGPfITu7m4cx+Guu+7i3e9+N9lsLFN9MSj5qKdZgfBKvoxcWR9BpN0rgt81IdCIRAgtSHQxt4+0tqr60NIKV70CihUYm4DGDjjwGDyy90X3okawSkg+ElcdRVJaIAKHccKpt0oaWlvRiEndHbSBNYVEKisi+1I0BjcIz+EEYTQTFX6UkDSijURu24LHgNS4fkhYxxkEnkLqXSq10am9ZeR90BEgquYrEJKbDtLbErzeA0j67ziS6kvakMyBtx5a18n+t4zBn62BY8fBKcA6DLY0tsHarZBuZ2a5Soz6g6rlgv/7rlyBGIFzpIHUkyY1shcnavrKi8Uijz76KA888MDktjvuuIO9e/dy8803k8/nufXWW7nvvvtq+bTLHrpY61W3Xpl7yKKsi7WOt9aFFkKnh6hHXdQdXPubpmalg+pPIgmWBeWSpOuGR6G1GYZbOBei5KfPqdd5Gp1ouivab6UaI3VxsID1yJKrBivqwm1Pe7xGXiAkUULSW/o6VaCgaVAvOF/DwTlKISQ2FvxNLyIgmSBsUn4MUdKNENaVos7fa4PjaCEkpGiEppFsltBd/BDyHu5Fsm9WCZ7YDc29cEk7rE0nueKGHOuLbXhWM00bttO2eTtsvwpyLVxMV8+LD027nU8NR4UJGhUF30Qj+u1YSM+U+kRNCSmTyUwRJCjuv/9+7r///lo+1UUFVahBmAIC+XgfQRbHZkKhgBKSh4gA1ChUhQ46inuU0OZmamIg6LaxgiV/YhSG+qF/EBqyksqbpfJnekJJCalAmCrzIvdpHUyPdS0S4amoYZCwThZNfUXTkqMIaTQSCixOB3+bIaxpjQX7PE2YqhsOzuNuwuZcVTj+iDPlHBqRVoK/zRBO9lXCVVNUJaRk8JgTyPt3ECEnN7CQsPZAPgFv3gJtO5Nccm0TZm49RvNq+JVbwN6MxFkxFgZR8f2F9AMF3zIDptoa67fv4mh+PRcu3thwiUDTSx7hVfwa5Kp+nFAQoYKAKE20EY4u0PKoJgW0bmMhdY2p844ccCfgB/8IHU/AyvVQcaBUhSdfgKGDzIaMZrqeVGuj44SLuUYTCk3TjSFpLbXLqQT7aUEW+iHChtI0YYrsJEJIhwnrWMeCx64PHr8b6YsaCP4mHZxPTd2pQtAijIbOhmakZ6kl+LsDhBcHHYTEqWVqnXN0DCE6lZwr9Jz4QN9EgSeOnWDLFQM0tf4cjKeA24FN1M9s3YsBUUGCktL51u+0I07/H0MRE9ISgS7uEH6U9QrcRhbZaAZaU2DRWomm+xLT/l4NO6c+mwO9L8DEGBTHwTfB8aH/FEwMz/nY9Ti0+K9KOk3d6QKsr09FBn2EIoGosalGHUp20X6dKqHsXJ0rdNsphMhOEab6VhOKEaJODrrk6Hk61+tzgv1rU7ASkvZjaXrRDvY/gpDyaPC80+nd9aGvAPtOeyT2VyjmK/T4E2y8wsWyXkC6oRoJW3U1/opRW0Q9QCAkkOg2pt33Yri4hApzQUxISwgqaBgjFDaoM4FKvNOE/m9ROfg44VcgOmpbF3lN603C9+Hok7KHPU1MLu35SyDhvXjYQJiQUAft6TWZErIgK/kQHG83sniPE9RWgmPuQYr/qjz0I6+vhZBox4PnaQpeV0vwPGPAd4P7hxAi2gjcgsi8vxucwyxTe5NGCL3sZsIg8n6orDs6bdYPtqv83kQisqOII4MOWJyOogP/dgA4AMZPYOeP4Irt8D9uhEziMeDPEfe8HkT830U4JCNG7aDvotZ/9JJIb4qLu/ZTK8SEtMRQQgru6nqg0UcJ+TroQqhNtSayoI4jC22OUL0W9bs7m/RcnImVzkwo7ReFEGlCScLM0PThRPD8g4TkFE2AaPSXC455O0JeieC16n3qsKBHozQJYS1JlYbqiq739SLR1q7gXJ0gTI2ZSHrzMiQVehRR0mkUd4pQkj4TVJN4kDAqbQyOeSw4S5rcUUdz7ceanq6MYvL8+HC0H5K9Ps+cKtDWcYTGvGgsLRppYj0mr8Xk9cz/eMOLDeeKZqJxdHzOa4GYkJYYqsiiGYW6X0GY3rKRvh8VL5QJaxzqqK0pviRntuyFX6+g0q6oaOUpR5iAmxm6v+joBCU+rWtpOk1TiTmkRqa1m0HC/iCtxajJqV6ratpR+4aUADQdqCq7IWRkg6YojxBGZxkkYjqAEMtuwkhuJnfxKPQs9Ea2qQ2QKvoyhDUkrU81EPZCvRj6xsDq8/nFsRIrjRLdidNY5rOkzSR5axWG0QNcT9h9FVVvxTh/nK1OZER+xr1gtUJMSMsA0ZoMyNdDU3k6KbYVWeh1UTcQQupGiGsi2D47P3b1Fz97XBU9rjFCQrKC47kUiSIqyOL/k+AY1NmhG7HvOU3Y8zOMCADUv+54cP8wYo2UJRxSqJFjldAIVb39SgjRHQ9exT8H+ziCRGZjhESn52W20DRltJvERM61ik7UbkiPbbpllJKvStAVfb3wX34LWjdC5zbYugMuWZ3kA6/agGn1An+HyDYaCacmNc/h6GPMDjEBzRdiQlomiJZYNX2lC6s6OugVf3SmjvbEqBmpRgtTVXczwZu0L5oeqaiirBLsezhyLDlkqWxCyENJUJ0TKkjKTOtaKnaYTjBusN9BQlm8xm36+GqwfYCQEPcH5+No8HdlJLIZCR6n5yXqtnA+UHm+KhijvfcqsNB03nR0k6LJSOBmG0m3tNDQ003FKzFRnuDpfc8wVvUYLoNXAKfP49iOUfKZA2SS46Tt41imRktRP484Ynpx+JIfnUzFGWfxCIzP4XwhJqRlihKhpFmHyg0yNa2kii8PiTSUoNYGtxeD9hJpIX8weM4iIncuIenFoeD+VqTsvnPaflqQiOh5wt4gVcKNIl9/JUgzeA3ax6Nj2XOETuHqaDEaPP9zSAPqIPC3CMmNIBFjHiEnTfmtQEjiCcLer7lAFXdKjqsIxRZpwmF7R4HHCQUaUVxDKzvsNlatfinrXnUDl73z1zldPsqzR/Zw2wduZ+JwkYnDcOKHcGBTgZ0v/Rkbe2BdB6xtBsu8AhnwocnGRmIV3mwQCBX84JLNiM/XQiMmpGUKrStp2kzrKdHIRxMPKvmuIIt2GSGEKDT6WkPo6zZEaGLqEDatqkRbowuVnY8Ef3c42F5F0mbaHDoU/E175JjSkcdqxKHLa4UwHagpu2HClJ1GTur9V0BqRGrhk0NIskTYsHskOMazqd9mC40YoxGQxig6CDDq/N0MXNq6itduuJKrX3sdK9evJdfYTn7FCjKbknR6XXjtDnfcfiv9w4cZHjvKYP8pPMr83V/Cik5Y2Qnb18Oa1Uf5ldf9CaapMZn6cbQBbwauuYBXtpwRvEOGRkjn68oQ43wRE9IyhV6pa1G/QLgkKbSO1IQQlUUYzYxy5nC4aBrtECEhGcjirQIEnzAaiPY8TSAR1LFgWxlRs/UFtwLygcwSSqRThOk3dW/IBM/ZSDjzSEnqBKGX3ESwT+0j0iiuASFRHXuhzcGNSD2rlwtL10Go4ItGQJowixKSbmsFLm/u4Lbt17LqbTeRf+klU/aXpBmzwee1r7mek/176B1McOyQy8neUb73zTFaW6CzE05vg8suH+S6X/l7MdpA3GkMI4PBKgzWAZcztak2Me33ekI0GT3ftRt1UTCDp/Rk5IoRuT/GvCImpIsEPrLoq0JNr5t1eB/IsqRu1gnCUQxVJGppQaIbvfpXZd4w4WJvIAv9S5FFtgl4GiGh/Uga7inCKGoVQjAdhPUnFZXrNt23qtm0BwtkaW0CvkNooKoS8UPBsWn/E4SptG5k2uyq4JhUCXgyuF0IGUHYKDtK+CVTktaUooocUsCbzDQv37Cajbe/Gnt1x4z7zOUbef1b/wOuW8XxyrjOMPt2/5LDP/wNqkMVSqPw+GEY7oVX3yHOT4YJTV2QS2iX0neAFxDJiB0cySuD3+sRPnIZk0De6YUghSC+950gWtIW6xjzjZiQLiKoakyvzBPIQn+aUALej0QoGWQxV0LSqEOv+KPm+BpVjRMmhxLBY08SGpUOEHrDqdloS/BcKzhTKagfzmiflUYYetNZSSpeGEbqQinClJvaLpnB87QgZKeRWJZw+m70GC4E0ShNI0i1IioCLZkUG9tb2D8wTKXicNnmzazbspHk2i7IzjxQwzQtcg2NwW8+0ExlrcPr3/BKKoUxvEoZtzJBpq3AT77Xi5mWwaRtq6Gn1aVr8zgYasbUhyQKVyPvULR/yUc81Genuawd1HlQhfFO5GYg5KmfvPmCMcPPODJaKMSEdBEhaoszffupYPsQshR1IEuW1kDGCMkqRegKYUcep/1NmljpRQr3uxHSm16TsRGi2oZEVDpdtkAon1anB021qeBcVWoJhJCs4PenCRuAtxGSTjq47SKcn5QJXnMjoWJPG44vBEpAZcIx7NHeqRTwyo5W/uD6q/jSj5/kQP8Ib3zbTbS/7GWwec0cniXDijUb+Pjn7oXhkzB8ErfvAE8+e4h3/MHXMZp9Em3QvRF27axw3e/2YhhRWct24P3IO/RLQttdF3gdEkMuJHzkXUggn0A1YdKkrVrmLlC0YugnMCakhUJMSBcZppOROlNrradC2A9URr6K6skwRijRziJLRp4wlaZ1pDHEGbuAiBUSCMkdZqq3QwvwpuD59yNquAJihjMRPF7HRuhE1TxCNkZw30mEcE4SDlfXqK6R0Llhc7DfjYSNss8jxHY42P9xzhRznC8mCOtIWqPS87YT2Nmzgq633sLb3vg2xswUrZdeSqK9/Swy47PBwDBTYueUWo3fUsLsGWNFy3O871ef4fR4PwOlEcpVqPTC1x+CDatEAJFrANs6RpL/OW3siNZsykhCVN+xCnAtchZrCY0lVbM5QngJYSFpOu2c07h+AWDoJVEcIS0kYkK6yKFWPlFo+k3TZloz0tHp2qejXnnRtF4BWc6eDR7Th9RrVN2nkYNH6Fx+AiGjk8F97YQjJI4ROitoek37eMaRyM5DCEUbSZV008H+KgiRdgbPORq8DrUT2h15vlpBhQsVwuVVBSTbsmk2dnXRcMVLuKJnHTS2nP8TmQlIdUIqXDqbEjlefc1mDvZmOHo6xakRF79a5ZlnR0naMkEkmTUxjDE8HpP9BCfNMMA0IaTuMr5fxffLGMZaDCPo9PK12pcj1FQGfTxGKoguZgvVhOonTFu78+BHbH8XlBdiIloMxIQU4wzo9XBr8HsfsphqVl/J5QiyqKvSLjok71FC+8kupFqxlnAg3jBCJl8irP/0EjaqqvlNE+I98H8TTor9RyS6+RdkOUwHfw+hOWo++Psi0vOj4ysGCZ0aDhFGWTP1A50vNME0PfW3HtiaTnH7f7iZtmtfCRu3g1n7r2CubRMv+fUvc3m1ilupMD50iKNHf8Y/f+f3KGR9xiyDldmVmMkqQ+5JqlVwg3xuJg2tLWAYDwLfAnyqVZ+JEjRkTpJIbAAK4JyC6lFIvR/MDVA9Dc4YOBOQfQvYszV6VVlHG/KJU2dDTQBzNk+rGMsQMSHFOAPqzqVODJp2iiYwdGaR1ntUkq0OB9FZS1o7UdeCDKGibn/kfp1sq0kbtdvRykIhuP84QmYlQuc2HbxXQMgviURd/YTD+AYjr0kjGF3+zmaceiEwEYukNttiTUOWy7Zfyqb1a2m+9mpSl2wGKzHHFN0sn9dKkMy1kXQ9fM8jkbBwjRI7rng9q1cnaW5NYSfawTpNwvh3TEw8ywDXwjSLFAuDVKtFHKdIcQysJKRyMF7Yi0E/llHGcIcxq32YEz8Aby+lsWG8ShGvUqJpYw+JXA8YFfArolYzLwkaTSegOAHlMjRsBSu4vCmU8YtlnNFBjLSF1dOEMTEIpSKMDkC2GTouAc8Bz4VqEewkpALhhedCcQQSKUhlCV0Ch6AyLrfMGoneqgXwTPAtSHeCGTfA1gtiQopxBrR/aHodRP3ZUggB9CELfSvhYLsJQjNXde9W/ZY2o2pPUQFJl3Ui0UMXocNEGol2MsHjf0oYeTyGkJIKErTmNIxETiuD5/oXhPhShOm4k5HjgPmtSCSAm4FLMimuWNvNmv/8m7S99Y1yp5U915/WBpaJYZmkW1ayqiXPr13yJ4TGTQCP08QpQmOjFMVCL4MDg4yOwMQ49O6H9hWw5UroH3iSSglSNtgeJFxIFv8GvwB9x6BcgHLJ4LJWsFNdYA+BO4zhjUPiA8gn5Qj+0CEY6IONvwvpICXXPwynTlN+9knMrkYyXbvw+/bA6SOw/1HouQJesREqBSGU0V7ItWKkcuL241Sh/xA0tEFyDWF32zNQOIgxdAC/5+1gt8LYMagmwc1A5w0Yifm5MIgxd8SEFOMMaBk7Wo/RvpoyoaRae3d0WqvWejoQR4RxpAZUQJbBDJJKW4fMOXKRsnmOMMWWBjYE++gOnstBalIqANZUXjdTXRvKwfEeRNJ+leA5VxI20yaCY36esGH2QnuOZkIb0GWa7Nx0CZvWrGT9tVeR2Xg5pIIeoznVWGqBDDJlNklIw1cAf0rYCGCSTD5Da5tNPl3CzVfodkY5PTzBv/7DMIP9UC1DSzM0NkB7E7Q1yV8PulCoQLHg0777h7QcS9HcXMVMVjCSLjR9FnwbBo7i9xfwhqtUTw5RLCY4ebyP4d4SpaEKm9sqmEcsTjyZYuTUGBPDBQZODtGx6hmuOPpzek+OMjhUZP9zJdZdvp1X3PJ6eg8+y+DpXn7x+DOs3bqZnddfw8nTP2d8vJeBgedZvaGZjVva6X3sbxgfLTNwooBtpEjaDVzyli4ybZvAb41JqQ4QE1KMMzB99Jhu0yK9WnZqek4titTDLk3oJTeCEE4BiYQ0ulJxcRPhnCMXWdx6gv10EYp9tc6joxzUwVzJUYfiETxeIyJ1F9fRD9rpcvr8T8+s0IrBOivB6rXr6Nm8kaYdOzDau8FegMhoRmjXVxRnDvWz7BQZ+ykyxgS+XSTf0cfI6En6Do8wMmjgOia2k8WsVEj5JbJpSCTAtcC1wbFhvP8IiSI0uPJyzRyQelzG4A714o+BP2FQGSozMeTQ9/xxTp+C8TGD1s2NGFWXoRPjDA7D6DicGoOJ4ZOsTB3hyNFRTvaVefoXUBwZYvXGNg7teYyTx4/y05/uYWSkl5YWj8NHn2J4+BSneo/glzfQ2ZWh9/knGO4boveoTy6doiHfyPqxo6QbWiDVhDFZubwYUJ+FOcP3/flIn9cMo6OjNDU1vfgDY8wrtM6jPetpwh6fEqFgV4UHfYRRltaD1iGR0kpC1whNwymptQO/SUgc/4ZEO6eQKKoJiT5SwU2NU/UYdFSFwkYI6SXI0vsMIu2eb0J6r93A2xrauOa//xnZSzdgXtKD0dAGmfw8P/OFItAw+kV8vwLeCKNHv8Gpp/6EbDZHKtNCbtUbGR76Jb3H/o10GuwEpINSju/B4BEwPFi7CjLNkGkCimZwleLhV8Arw9F9JqODcPK4x/AAjI3A6SMGpYJPcUKaeu0kNHZKyWj0lMFY1afoQqEMxy2LPYkUruvgeS7VqkvGtsgnE3ieA75H2vW4epvJ9TttDh6qMjHhUynCtktTbNua46pr3kZ+1Q6sy9+BYagk5mKAXgJqRXj+oGv4yMgIjY2N53xsHCGdB9RnrfRiD1xG8CI/1SHcI0yXKaEUIv+PpvtABAbRibE6QkIJSQfaHSEcXaE9Qmr9o4SoCrpehIRUVJEjFCxA+LUbIRRMnI+L97mgbuM3GEmampqwLtvGq1asYX3PKlKXbcXqbodcIySWQvE8OJNGCsNwwcySbrqMto1vIZXKYCcbSbVeh5vswjGT2EkbyzZJpg1w9kF5N8bqFIZhk2xPY2VTkElAtRfcilzJJETfkGv2RGJuQDYHY0NwcJ/PaAFKDuQbJMIyLLDSkGrzMW3IBwobb8jl6LECmGDZ0NgNpYLL+LBLUwoySWhrg4ThcaS3QtGRrGGmEZJZsBI+g/3P46STtPv9YKiT4sWC+pO2x4R0HtAUkC6sFwN0YVdoTWk6ZtqmXSa9CLn0Imk5m3Bqq4v0JBUQ0YKKI54N/q+jMtT1oAjsC/ZvIOlA7fcZ5kwZ9ylELKGRWy3RiBjwfNTIsrl7A8l33Ylx7dVw2dYaP9NCQskzQ6r1ZaRaOwk90S8ln+8l3/kySGQC6bqNP/olGPklzasaMJI5SLeD0QJ+XhRwRgXsYAn0ob0IXgt09MDEMIwMwPd+AKPVoHesARJBljGdhcbVkGiSp8w2QuuzUD0lh5rMwsatcPQE7BmGS4Ia19otcHoCDp6C9hbI5SHfCPlWsFMex0/+nHGrTDvHERlNKxcH5j8yOh/EhDRLZJBrpzZCH7QScj2pfS8eMkdHXQxag23PEHbpNyCL7qOEEYSOGh9fmJeyKDARObYOzTtG6E6mpH4aSaedJCSf8cj/PYSIEkwlFbU80vciep/64KkYopZkpM4ROxFLojxIQaWtEzLL6Uq7ETFf1YYAE8x2YQYj0gyQfQckN4mc3dRhIUFI1NYD3jj4gbzFM2Hi85jFYZJOI1b7VhKru9l4zTdYWSrR2p5l46WraGlv4OAvfo6dqtDSBQ0dYCTg+Wdg9SboWCkpwUwe1lwCxw7A3idhdYdEWLkOOHAU8ZO1wLTAs2DCrTJUhpe85i00r7gMjBWE2ssYi4WYkGYBg3AWTxNSGm4gVIU1EZLPaWRh6gluHnKlrwqzZkQqfRBZSK3gb13EsUDrNNHCXoHaGH4uNiym2mVOh9aBxma4zydMBaqaLgoltgQzCzKmu1HMFeIcF9auRpDPRA+iCtwGpPEwXAeKJRifgLFxyGTE+sBgCau4kpwx3M9Iyy26yb4EbK33Rqx3DB/Svci72wPkMDwL8g9BYgDLbcdK7QRvNSsv/QWeW6RrRRPrt2ykqbWRUvkYVqJMS49Nvt0CE3p7h2k0DTINCXLNKdINFt1roKGpRNKaoLMtSTprYTVYlBJFRksFHM/EME1S6QSJVJWq75Bf0UNjt+ow4+VwsRG/A7NA1NVKmyltxDm6EYma2oJtHYSjHbqC/28ijJA0stpKWKRvQa7wv0vo1RZdsL+PmIYuJaiiLeq43c+FNaDOpP6LwkHSdfOBHPByYAciVf9i8Hy3IsawlwON3ihW7wvwv74M1+yCHZfBtdeJRjp7Mai3WpBLriiGkU/3a5HLhUYgKeS8oQN5R9uAPAk/yRv/0w4wDYx8B2ayFcOEK9asxLB8jFwPGD2Azw3bPwVGBsy1GMYVQDumCbkNj7Huum9jmpvBaAWjm85jP2HrZf9KJtVAIpkn17KOfXuOcuD545ipKiRLSGL43AX3GPOPmJBmgEYpqg5LI3NzWpE6hzZs6nVVjnC8gZKQjXw9k4TKM/Vj01k42n+TRaKCIvKV0PTfIDI7SCMtdS6wkPpJ33ydgBpgJuKYLzcEdZWwCAn/Qh27beT97kQi1CbEWnQrkry6GXmNu5D6UTY4DqNUgAPPQ0cbNDTDVbU0Jap3zES6KvLXsRGBt4ZhgLUyuC8HJDF8k0TzRrkv1QBmBgywc68MjBWbwGgC38e0bkYM/DqRb2cOqGCZCSy7DegEQ1qzMy2NGH4bCbsJy8qQzLbRsel5jPwLJHMbI+m6BTJujXFWxIQ0AzTCaUCu95qQK+MVyIKki103YVlQU0KthCqwZsJUnRISyOLZGdwXHR/ehpCYXqc9izgUrEUk062EarJ/RCIORb1p9xdK7KHkr/OG2ggtgs73nOiIiNXAlUgathl4fbBtJZKmM5D3dkoirjgB+/dAUzukW6FSEcPRixaaF5gJ0/zuDKBh/ZkPS79u2uM85J3QRDrIJ2AUjM3AW6Y8PNW8nVTzdYS+IdDV8jRd23YjzcGdyDs88xyqGAuHmJACRBcVjWSShPY33chXYD1hoX0FoenoWLCtkZB8ohFN9NpRi+HRbQnk6xl9Q1YBHyAsJacQhdoPgRuQVJH6yQ0Bv2D++2vqDSp2aAluWxDiTiN17JOIK8RssQFZnq6M7P9VyHuxgXBk3bnL3x489xQMHIeb3iTMuLl7DkcR49zQil70W2syw+VBgDbknYvONmpEvkF5pjonxlhMxIREGBFBOP9H3b2i475VVqx9ODqkTg0+HcJ5QWoUOlMSY6apLvq8UWSR+lO0IdVGFkxd3jT7PYBIm4e48HTVUoKelySyxKxDIkqtZjzL2QlJvfl0qbIRM9RVyFJVQKKtzcjFhy5dKnI5J8ZHwKnAs89Lk0xzWuY+pCNzImKcJ2aSLJ/rXZlBlDHpWR+jnnDRE5IuSGsJi+YGU/uYtfelENz0463RkErCNVNeq7VGDUmjWAW8O3K/JoN8hJjGkHELF0t/VLTm1onUdrYgBOIDDwLfOMvfdga36wjl/NcgNbs2wvOrFwRzfl/LZfhvfwGbt8Lb3wavehls3SRXHjEhxYhxBuYs/RkYGGD9+vUcOnRoctvu3bvZtWsXLS0t3HPPPUTdiM51Xz1AFxqdeOoR2s20IVY2HcHPFoR4mgkdrvUETh/PUIuL4On70tSd1pHsyP+TwBuQFN/dwPuA25CFebkg+r5sAN4IvAu4K7j9B2SsXAvhOVmFTKXdhNQCmwijWBUt9CB1oTWEXtjR86rv85zfU9+H8dNwZA989x/hG9+Cb31fZOExYsQ4A3MipP7+ft7ylrdMIaNyucxb3/pWXvrSl/L444+zZ88evvjFL77offUCXWii/TFqyNmOLFhqQdmG1A5aCWXeoU/y1Az1YuAtwP8H+AjwO8AdyIK8XJBEoqEuJAr6VeA/Ab8L/B7wHxFCiop3exBp9lbCEXBNSJDSSvje9iDnqhm56KjN++hDeRBOPAff/T/wj1+Hf/wOFIo12XuMGMsNcyKk2267jdtuu23Ktm9/+9uMjIzw8Y9/nI0bN3Lffffx+c9//kXvqwcYhKo2dZpOIwtXU3B/krB8qsICrS3p1XM9wUAigAFk3udyEjlcAdyOEO07gZuADaSRd3Dmj3JT8HevAd6MpOR2IBGWih8qyIXGJcxzr/7ze+CRh6AcR0gxYsyEOa2nDzzwABs2bOB3fud3Jrc9/fTTXHPNNWSzIu3csWMHe/bsedH7zoZyuUy5HPoSjI5O92++cJiE6S7tPtD6j6aFEoQ1HFXd6Yju6K3eSgHTXSU2Bf8/SjguYqlBm4wvNVK8xEjR5hXpME06UnkMV2cfFJipcpZCUq6XIO/rM4QRsfaAJQgbl+cVxVEYPAUneiGTgraLxTctRozZYU6EtGHDhjO2jY6Osn592DtgGAaWZTE0NHTO+1paps9mEdx///3ce++9czmsOUGjHPWa0ytibVa1CTsSLCRtp7d8ZB/1LhDdiUQCxxD58x8ipNS7iMd0vugA/m/gusRKrkmsxSjuhXQG1r0CRsZE0TY2KOOtpyGDCFbWIHY/+5BoWKPgPOGgv/lHEcp98NX/I04O77h5CdsJxYhRe1xwxsm2bVKpqVqwdDpNoVA4531nI6QPf/jD3H333ZO/j46Osnr16gs9zEn4hKo5NTPVJtgOJIW3jlDeuwapI60mVNNp9FTP0OitNfj/+xFroq8u5kHNAimkDqQuFleRYbWZ4apcN6vTnVjpdui4ARI2mA1CRpVxGcQzA4zIzzTSXzSAGNpExSJq5JpmntOw1Qr8+AcwOAhGA2zdCD3t0JID62KwF4oRwkei+nrMtSwOLvi719rayu7du6dsGxsbI5lMnvO+syGVSp1BYrWEElKJqcqpLKK0WosUt7VPaCOSLmph6c2SNJAr/0ZEKl4E/g/hrKJ6kIZHpesm8j5sI5wY+1tkWGW2QG6zDM3J5WDbpRKinjgKpnNOQorCRqLGk0jUqMlgNW4dR953jX7nZYlwHHj8p3BqEKot4Bgyh6cpW/9hd4wLxHSFcbTR5Fy4eMjqgglp165dfO5zn5v8/dChQ5TLZVpbW89532JBFz0lnHHCFFwrYUe+jjhQU5GlRkYz4ZXAx4F/RdzGd7O4pGQj6bIiQg7XIBcE1yLnvBloYEQW8f6T0N4l7tnPPgu+AxNjUHQh1QJF60W9gmzkAkMdup9DoqUhwn6jJKGEf15x8hB8+3NwYC9cchn81T3QEpt7Ln94hF2OMxEUkftVZnXx4ILX2euvv56RkRG+9KUvAfDnf/7n3HjjjViWdc77FgNavFalnIl8BBzCgr8beWwjYQqnlv1Fi4VWJPpYg0Qgi/k6VKWYR0h/HeKIoOo3tQKydOCEMwjlUSgUoH9AbuPj4LjBpE+VnZwdmrbLEzq0tyIXKOoruGDS/WoZhvvg6EE4dADcWo8NjFHfUGIKoiTfBa8qU3XdahDx681laqdkLXs5/cht8VGTGtIDDzzAO9/5Tu655x5c1+Whhx560fsWA82Eijl9q8sIGZ1CUjm6GGrdaCkMnZ4t8sjCfynyuhfvnQiHFXYhBLkZUQQ2EJqWNqMf0DKwD4YrMk7ULQfNYhah9lGtZ/t5MdjB/rXvyEUEDoviNjd2AgZTMwoyYixHRKedRQjH9+QixfPklskGgpfpk7w0jq8VlBTrI198XoQ03W3hlltuYd++fTz++ONce+21dHR0zOq+hUaZ8HoDwitmnQSbIgykozOQlgu0x+olCOkOA3uQ1N18PV8Lcl59RPGnJVwVlgwhpKC2RyaiaGxGyGpKzOMPg+cDDeDZUDYgkYJEA7RtAncCJnLgDMh00hdBIxIZOSyym0+1AnsehdJmWLNlsY4ixrxDP2E6mtMPuEkjJFciZc+FSlnqo2aEKDxPsgG2Oe3DGlmtfK2I6jY3rK8aVpBN0EgrKqiY6YKolkZos0PNBEUrV65k5cqVc75vIVEmJBytG2m6pgk5GZrF1UbY5URImtS6BCGmcSRNOR+EpOe2A1HM+YSjyaMf/7HgNo7UkWyEiDLM1O46Dn5JHuWnwUuAmYJkHlrzUC2A44FfPCch6Xuqx6Xjzf1p9y8IfF+ujPc+FUx9XC/jv83lULWMMTOCxL8fSHr8gDQ8TyJlz4GqKWQULW+4Llgu2IlpGTategbRjlcBIyAtT2cpQ2g4FkkBGpplmKmmFbWHXphvRb0ZDcwrtE7kEBrOu8G2CcJFUXUvy/XkNBPWbgaRibQ6UqMWSCBqNq2/6Vhx7QWaPjl2FDiMGNfmkH6pywnJKdRc6pXdaWhYB1tuEyFAYxoayjDSC7uPQ581pw5gvRDRMvKCEtLEkNTGPvZXcN0ucIdh+w3QXrtWhxj1Bh+5PHbAr0o05AW1IbcqM7Ss4NJtUnVkiKAHRxqsg01YFpgJsIK/dR2oFIIvmCXRlhfsJJWETIXJaWGGRRgpna2GpIS1MFiua+5kH1F5hvuiyjIt5xnT7q8HSfR8QAN5FRWsB16GNIyOIAR1ofARWb1DGGlqvU4H5+mtjBCSvld5hMgGgRPB3+pMKtmXpiQc8JOQb4WeVmisQN6CkZVQOB5qumcJlZ8veETsu1B1YWgMhkZheAyqVcLLouUUoy8WdMGNpswU089vtMBvRrbpY8/3PYl+6j2gKiIGrxqk6byATMry/rsBIUUPNWFNjZwNA4w0k7KsalkUqaXAK9GwZb9+8NoNwDbACC49bR98K3iZM722hf/8LVtC0obQk4TkEq0JRc1QtUyoNzVZvRhwHRK5/C+knvTvXDgZO8Avg/9H+4ymw0WIZwiJirSWtw5xlCggYpMOxI8uiS4RVahMQG8vbNkIG7dApwXlVfLgsaPQu39Ox6xEvSgwTWhqhmQDjFvg1FeheekjIIDJ5S767banPU51t7oqRC9PLyRaUCm3y2QF1S0HJGKB68HEqJCRE1g8G0akKc4Aw5dUnMI0IJGUumq1LC7ylQpMTMhnakq/px3UqDyJviwglZJZXWb0PEQ/c3pZv3BYtoQ0QpBCnbZd5x+pmq4LqR+tQRpjOxEVWhOLlMIBQhe96f0KtZcGp5AUnooMai3+PNf+NNaBcASIDfQRvurm4BiHkSgpoX/pVGD4tHz5yh6kWyBtgrcRcrPv56mL+MPzYHQUfvE8FB+EtZtgdTcL4K63zBFUCP2q9K0ZwWrgRpRrVjJi3xT5NHgueAWJYHw3qP8nIZkTYpgSRc1F9ebIPqt6qwQc5UCpFBKSnRBSmSQgA6ql8Fi9YJsxFij0SnilIl6lilMsY9g2diqLh4ePj2nZmHYK00ciI8+YNmxNL9HV2XNRKqrLl5DOZvCvjbFNiJprHUJMqxHZbwfhaPLFUeZrmKwKl6jsMyq50J8X1pegtknaEHquiGY+EKVYh3D6rUauo0gKr8C0GZ+eC+WifIE9RAhgpyDXJFeNSw3FEhw9CccL0HsSShOQTAXF6VjgMDcEn2A/iIx8R2660LpVJlN4ZvSS04qca08iGLcqIgPXANuTz5YfiZqMhNzOeSga8fpyHF5FiMgJUnOOA1UHqhV8JSQj+Aa4dsiXTjXY7uO7iCjCKMrxVcbxikW8apVq2cW0kxi+gYsnBGrLUm/aqSDaiqboonbREXHEeacmp2P2OZdlS0hng6qqqsFtA9K5vwK5Atdx1ouawgFkqbYQatQvjdq/5hBatZDkVjQFoRWc2SEbPMO7ESHCLxAyX6yUZQH4CZK660QEEBuQlF0IG9JNsGIbrFoBnSk48rT09Bx5HI4fXPDjrgn8kly9//AnUPXgda+AXDNkWhb7yJYggipmtRxEO6rvJKipEBEFBCRkqP7WYTK95mmjakoIYXyQyTSeg1w0NGh6NUJs2Ey22vuVQD1XhfExOabShNSEkpak2apVcMrS6K03y4BkoLjUY/QBz8Wreviuj+W4ASEV8UpF3KoDZhLHqVKZGMWyTAwzOB4rWP1yOTnuRGNApkqo0Qvd8139ohfImuGZfUF32RNStE6kHxO9Bpi+6OrjFi9gJfLMUYG6Oqyl5P9mBqysfJgqmlzUvw2cDWYZ5yjV9SCNqbsQn7fDNXktc4eHREkqhGhGotl+5CykgTQ+pgHYSSaveJMW5DLQ3g5d3TDYDf19wUK0lODB3v0iZ1/bDd090GNCKif5/hhzw2RdPvJNNoICvxH07agKbfJKXnuCKkF05QWRDcHi4OLjTtb6jGrQnO0HIgQrAVZayMxzJNLyA+IoB3WjsijifNfCL5Wh6uBXHXzXw/d8LMuIGMEHK5LnBl9xBxwH3/FEOOS5UC3jVat4jotvmviGgWsAJDAxAg4OIh/DCNOBRlTafSGYLtrQn3r5Pzss60+4Qdh7oy9UTWa0fqFyb3V6zhLGJouHaHUFwqlNWVmoEg2QzsmCPBQUNQHMgIy8IeYS4yghZZAR6F9l8QhJX/kAInhoRGh4L/KxtoBuXJJKwqUSjI3Bug5ItcKWHnBKcmX54+9Bcb6ns0a/zDXSZv7gJ/Dk80KwOy+FV/jQsRas/Iv/bYwAwSWoZZ1JSCDO6qYpkYgSklcNHTN8B5xIVdV1wt4AfZzjCtmYkYs/BxmNkiPoi6tCKXKBWCyJkq5c0Z3hlct4rovrurj4+Bhk0zaGZWJMHr8+ry/HUi3hVx3KlSp4LpZbxKu6eI5clPqmgTu5iGkifnpNupbQfUeFIS7ybZ79d/CiICS9ZZDFrQmpGzUjBJQlHFfeQpgkqx9oS68nV2uuBRUX3AQ0tkmx1U5DMhBPnzgZNIbO/oNgIFHI54Fn5+MlzBHNyHuyEXlfhpHjywXbk5VR6P0p7HfArkLrFmjNQFMLXLoTMnkY86H3MByeLy8Kg6mqqwo1+7IbBiTSUkRPNgYd9jFmj8Ct0pge/UyDaYZRkucFcmtXCMmtyOLveuCa+K6PW3XwXQff90mk0/J9LE0AiION4wWpNwevWMSvVqmWS9imhW1ZOGPjQkDlAoZtYdg21YqL53mhOtsEL/A2NA03iGQMcKr4joNTKlMplahWK3jeOPgOhl/GdQx8zySRSONhBuNMIh54hi8ErbeIbrV2UI2y5qCClOcssaw/5SoNSCCnPgeTA6+bCL3SbISUckgNqf6gVx52EAnbcqVkAtk0JLJyywZ56/5GqUG4JeayQJaRdF0tepEuFGq82oC8Z2WkKlAiWFrcEowdhP4u6G2F8ZXQYEv02LlKSHrNUaj6cPg5wi9JLRHNtddy374sdI4bXNCqFXCM2SF4X4xgBTA8MGZK0BuRwMmPpO/c0DHBqeA7LjgWnuvhlSu4nisTptMpDNfFd91Au+CHpOb6uMUibqVKpVzGtyws28YpFPEqZZxyCTMp6bSq6+EFUZoGRG6wL8MzMEw5Tt918RyHarVCuVKhWqlguCrYqOC6Fr5vYVo2mBYGkvYzTDAsQ6It2xYBkKHFi1oWJTQSC6IwP1A4xoQkMAndvXVEtVrSqFlGhck2yzrxu50JGm5PyM0bgewWyPdAR1tQWzCgqTG4KrocBk/ACR+8MWabw10LfBr4AtKXtJjoR6T7VeS4NiOy/A2oWtUBhqB0DEZysB8or4TWKyWiaE/D5pdCOQNPnQT/EPjzRbXRq8JaYAJKHvzoJ3LF3bMStuekbyTGHBC0T5jG2fncrUhtxgnUbk5Q8/ECE9+KREmu4+JWXSrFiiSLDQPbkudwPfBckVdbySSuO07VOUWlLH/nTJRJEuQ5HCcgH0m9GY4TREY+WOBjYWKAK5lAC0gk5GcFcAyDomVTMg2q+CSdMoYvUdBkiQkH07Cx7BSZXAN2Jovd2oGRyUJDI5h5au++oBdnSkqF4PxNwOjwrPeybAlJ1WN25KaRUnT8RF30ocwaEcGDVwB3DKwSpDJySwavys6AmWaus22ThIq2/cAzCAUuBjzkCziEvJd9iCS/DYlsLVwMJjAmjsFpF/b0Q3ETrF0PWOJ+MF6VGhMjonSqOTQtEeRaarlftwonjsDxVXD8FGxai0+e6d3zS+vzu9AIzo5hcsZS5wdZB88LVHhVuZqfdFBwwPfxfQ9cD89xcB0HxwkJyXFkUXddH9dxJUDyfRzXoVopUq26QlSeg+P6GK6P6zp4vj9pr2qZJmag9DMsA9OyMEytHZn4hokXiCVcw8QxTdzIzfPB9MCQJ8fAFw9W35ebIRHW5D4tXflqHXFPtxywmBR6zKG0umwJqYWwb0WJKE0490a3Badt6aE6BGVHrnYyrdDaCpWEOGCTZtJ8dA6vLok0CN+INA5/lMUjJJBlXn3vnidU3vUASVwsRmBwBAZ/KbnGS14Cl14FXkrY7Ggf9PeC98I8HmGF8At4YT1hU+BU4eCz8r4+ewCu2o7QcVRus3ha0KWFmSRKfsS2xwG3CH5Zbm45nGjg+eC6eNUqTqVCpVKUd9kwSCTku+W6Lm7FlWxfxcF1q1SrJVzXFcUc4DoebtnFCbapw3zGsjHtBKZlYqnE27JkZlwgs3MAXKiYJg4WrmXhmpYQUqAfsNzQAchzwXB9qX1NmcwQjWLmC1qbUvurudWpli0hOYSy4TySpmsnLIqrmGEz9Vo3ehGkEtCQgYYkZBMie7ZbIJmFsVaY6IJEMzg/Ar93Trteh5yzX0H6kp6o+cHPHmp8e4Swxnc6uK9t+oPHhuHnP4XVW6C1B3ZsB3tkAV7AixlUngfsJKy+FF5yNbzmOmhtY+oXW2W1sBRj/YXFTH01tuTEkr64FlgOVKpBB0VQ9yhWqFZEPFAsVHEcl2o1SH8bBsVgz77vU61U8TwP06rieg5VtzJJapaZwDAl+vEB35AIyXVdisUitudi2jZJI0MiZWJbFlZSIiXTNKm6Lo7r4FZdXMfBrZSxiiWMiRLJsi8lMt/GNE0Mw5o0BZ+8TbZJRQ3T5gsagVUldEtak025s/3rZQldHrR21IAssjnCYnkDYSS1ZMrGhiG2IqmUuPemE5KqS1hgZMFrFpPR8Yy88ImnwNHYYnbIIueiC5lhtNDuDVGogLSAaAZV2DBjAq5cgpNHoGMFJFbAik7o7wS7QUQQ/ny1+9bu7EzuyTAgk8doboUVXZCSznu5r2ZPd5FgappTNpmIsagl9RvfDtVsBGm8ahmvWsZzqlQrFVzXw3PdUA0XEI4PVCsVPM/HssD1PVyvKukyTEmZgZCS7+MhqTV8cF0HHAvLMPBckXybhoFhmRI1WRYuvqS+IBBO+BiOi+24WJ6B6csFiWlYqmDAMiws08I0LQzTDjzrFiIfFHV+MOb8WV22hKRNsFGi0Uxy0F46WSjMIdNDl0TJOJeHVevlqiOZhGwO8g3Q2AS5dki0Q2c7dI9BUzs88zMYGES0c7NbOI8DB4CfIamyxRR7pJD3pwNJ1a0kdAQ/A24FJvrArkBzAro7YXQbrLsVTv1YVHlLAB7gV8tYzz0Kmzrh0CHIJSBhBs3AiiVxCVWnCFYDQ/tyLFFmWq4o0AwXLJuUnSThG0wYE0FvkjtJSq6dwvP9yR4iz/eBEqYhZJKwkkIMjijesMDCwDA8XNcRgjItbNvCtG0s28LUbljLEoGSbWMZBilLIhvLtDA8DzedBscjhYuFiWUlqFoGrmWQzeawM2mSjY3Q2iFrRr5TXt+8z8DWZq2CpEELZSjMPvG/7AgphUQ8Wh+CM92aoiY8KlKsspBTPy4AXtDr0JiFpjw0t0LbCli5DdKBAdLpFAxPQGUEvByS3BrixahFL2YySOS4GSHsEy/6l/OHiDcy44jyboRwGu0UpNOwYg00NotpZt8A9PdDYURqMksEqqmsuA7m6BiJYydgZYdEw426oMRh0vkh6iYQfPPV584wJhtmDbXrMQwMwyBt2Ti+geubuIaB63lUAuGD4fuYRiAowMQ0wDIMbMMU0UJkjIRrGviGiWHYWKaFZVskUimsRIJkOollh6k6wxSxgxlEYrZtge/j2TZGIoGfTGLiYRkmtpXAM8AwDOxEAiuRwLCTkk2xo9HffH9udLUNpOWWhcxdmh2WHSHlkEZKCO1Jo9ZB0WhJfW2rSEoowRKIklwPJkqwtgFWdEPnSli1Ga64DmiAqg2VUTh+DIaPQjWHxBYHmG3arhkh9OuQ9N1PWTxCcpAU3QiitGtCRookkKh2CrJ52HIFtPWIo8X+w3DwBRg4BNWzT5BdbMx0bj3kM5kcGSXx/AuwfgVkU5Bvmtl5IMYcELG08XVKV6AK0DRe0DzqGyYGkEuk8EwP13KpVitUHYdqqSxKNkRZjgEOQkJJyyJhmZhq1hrUEBxTJnpZloVt2yQSCZLZLHYyQSqbmnxvTcvCsMygn0gIzk8kMAwD30lgpVJ4XuBSZBgkLAtccDFIKBlZialefQsCveRPSf9XoiSkOEssG0JSVwbtOWomHE3eGNw6EcJqJawnLbmkR7UKw8NQKEgRNmFCwpBUQ5MvhozFHJTa4eg66P8BjO9mLqMrEkh97VeQc/QAoVfEYkAj2ioSJZ2VVgcH4Xvfg8tGYP0W2HgpeCVYvQJO9YvoYV6OTnH+tB09tzq9Nw9YfX3wxBOwoVvemJ5WIdt5T70sZ0REIEZCruATPhQ9qJTANUWhahGk9NzAmNTFdcGXllMyySR+8J7LOmKI5tKySSRsklYGEwuv6uH5Jp5vTl4EA5OEZKdsTGlqwkwmMFIJjEQwfsIOzFc9D8/1wPNJWjZmKoWBiemDYVuQSmCXPck4plOQTkEmFfQlLgbSwbymKiRmf5m/bAgJQvGtRaii02ZY/an2QZpN1QkgS0bU4HmhO7Dngm0GtQUf0j6kDKmftGWhoxWSJSRdN3toanMFImyY72txre3pohwVUdiEHoNpQvn+jMtxqQhHDkLHKmjpkvHmHW1yG0nDuCkOCDWBMe0nXGgcqVo9dTmzAdN1oDghC6Wj1kT128Jd/4jkTIxI3sRAiMkPcidKBlYFTEdaanwD3/QAU1JjdlgUsIPUnuH7Ev0kkiStJAYWrufgYYqdD8E76PlYtqTsTMvCtEwwDQzbwkwmAhGCAaaJH6QOCVJylm1hJsTlYfI400kMMzB8TadF9JROBXOV7AVK10URrCLG3CRRy4aQ9Co6eos2xUadvqN/00EoBV9SSRAvmKuSz0A+JSu2Osm2ACsaYMs6+Il2ZZ9fDcVDUmbzGR01I7OoBpGjVPNbD6l+tQKXI027G4Cdwd+cgfI4nPwl9K+HkXXQ3gisgiuvhNFjMDIElWFqs6CnCExeItt0zs7coQKb4eDn5Ne4qw1evgMu2wTrV4O5JOP6OoS650dWhCSBL2QusP9xxTfSt8Eqi6+dkcD0wDRMbAsMU5RwyWQa07TEncBIYFhpqWNigl3G8w1cLHGW8z0q5YrIuy3xuDNtGyuVCtWzdmAT5rpBDcgILsIC+4ZURZq/szlJzdkZMW51Xcg2CinlGmQygKHFi4X83FTAL4AzKjXcWWJZEJI6L2iNKHotEO3QmEncYMzwN3WNdBJa26G7C7p6xFy1sRlaMpAOjLAc5AO5thPyK8FeAc5RzseNOg9cBRxCxonPBzTFqlGSRehd1zDtliOsAEzNTCcg1wJrt8K2XXDJdjjeF7gdHJar36Z2GBit0UgKBznZ0XN6fmQ0PcaaculQKkH/AIyMS+3Qlyv0GBeKaGU5eN9MW4bw+QSedA5UMlK3NSxJ33m29C6ZNqZtYAZOC4YZjIswUmDaGJYM7vOR7IWBIQo708TyfUzLFBJK2Bi5jNSLMCbrRuGMJjA8EULg+3LYtuwHxwnScoG5smEISaWTgd9QcGluWJHXvFAIXBp8PziO2WFZEJKq6lymnvJpivgzIqj5bhGbF6RTsKYbVq+GlWugqUuUdu0NgCGrWQWxwF+fhsb1kDwMznHOh5CagNcA/8b8EJJBqOprJFzSR5FoQZthlYwyyMsrM22CLGloWglXvwWuvg42bIann4EXnoV9ewETWrph6HANCWl+oHGWD/jjExhHjsOpAWjvFMsb35yTcinGTDCZmv70g9SWHziAe+DYGJUcvmsACTAc8DXqqUiqLYiQDLcoar1EKuj7kcF3hh+k1AnED7YNBiRIgG1JrSibEzIplwNBhRku4pYlJquWF6TwDGk2TZhCmKl0ECEF04WrbhhhGXqJvhifFV15EXKcJZYFIYG8kE5kAW0ldGNoQK6+WwhHS0QJacmhVILjx8UkseSC2wieCZsvkeKKhZyAfSfhp8/C8Z9C6WnON+mWB65GnHn2Iq4JtarCJJGhgN3I+6XLQ5UwBnAQcjpKaPd0KTO5a1SgUoSTQ3CgF5yMjIU2bUjl4cQeGDlR1/JvDyHaZuS1loDEyDCpfc9C7zHo6ZBUrbVkP711CI2QzvKptm1IpbDxsRIJ7HSKSsHErVZIZrIiRkgkMUrj4u5gG2Ftqhr0OKWSTF4eW0GzqOsE0jwkzZawoaVZftqWpNl8T2Z72UE9yc/INq8sRGQYQQo3GK6TKkHSER9LQ8dZLtbnxAoizpREb7PEsiAkvcbRYbzT60a6kOn9VuT/+vuSgevIGOT+Afng5fugowsmHPkgJ0wJHdwynO6H4gh4E5wtnaQkYEz7Xb+iDvNrRBut9UFY2LeR9yw6exKmRr1nwPWgWIZiRRryBodheEyIuzgBxTHqXRBgEH5mgUCpZMqiNrmwLbm4vk4x03k0wlvQl2TYFrgJDHxMDFzXw7dszEwOw7Yxkkn5k2olSLsE+Ri/JKk/y8LQgoIN6KgLY9rz2oGX3WTPUBAJm2aglrODqbHBhZalkp9g1bOiMq3oWNLFgH5zL8KUXRW5miwQRj66mDUHj9ExVQYSKWkKqJ0l0hCrqFZhaEBe8JEBKGbAysD2PtjYIrUklRIC4ai7U8y0GOvgBF3qysj5dIAxxDro3xH379Eav5QKsCd4PvWl0yhBa0lJJJW3BommtgSPOzNllwEvKWmPZEKuIL/z72IlNHBIFgdszlfcsRBIIK+1hBxlN2CuWQGvuR5e8yrYfCkkMsSENF/Qhk6EFEw7IA5TFn/DAMsimcuR9H1Jn1u2uGcUMkJIURQ0atJLOisIyNxgv0Gbhm3JT7cs6VjfChtKE1nC6rgd9PYkkNVL8z0e8onJUR+X1z4wDv4EOIU5dSgsaULSBUvdFqJX8XqVrwtuYCw/OSxg+m3JfcX9JBgNkG2BTLMMphu3Iwa7KWhrh2wbJJqgepqZCEk/yn3IQjhKeI2oAf9qQgl4P7VV3LlIGvA0oQxfpQIaLVhMmpEwglxQWEQJyYBkGzT1wPoNIoWfGBIPuLQBjSaU01AZhIEDs64hFYNjqxBGbSnkvERtTadENBcAfU3qTjEE2BMFEr0nSI+MYRXLgdHgkvu0LgEEUY2hq0jg5GAFKT3DmCQkw7bkq2SnAgWbLdN9NRLwIZzu7ESinkCF5/li+6VfNDshEZA37XAMX5xRtbl2UqoO4TdDcwbzmceYCzTHEaQrTVvMa2eJJU1INvL9LCKnIOrEoKfAmeHmTrstyYy8mQarERrahJSSDTBuQMGXy2ySMrog2xIQ0swfCiWkI4jsuo+wwXg9co7XIxHKYURoUGsJeAGJ3/KEZrjRPiQLed8mgmPsIrwQEZiQasVo6oG1G+QqcuS0NMS25KQvazQD43kYOjInQupDSNoNjq0JIUSVNGjP2/Qv0vksC/pa1bh/AEiOj5M9fAR7YAhrfGIGi/MYs8P0i7Hp8icr8jiV87uB+aoxhZAm/8ZIBcIBS5R3elniB2RkGJJGhkCQYEHBkfsmhy0agTEyQl56XJNiSk19RfXAmuiOrnb1orzUBLsXHKo9v/OQBgYGuOqqq/jBD37AunXrAHjwwQf50Ic+xJEjR3jpS1/KF7/4RbZu3QrAXXfdxSc/+cnJv9+4cSP79++f69POiAySjGojnHWkFx06kryRcFy5BrdlQluhQrAtV5MjWkCURsHthaOHoaEJ9uwT+XcyCc8NwXPPwUM/hiM/gtIBzkYjGvQfAJ4DHkMWWxP4vxChiApGNiLihnKNX0oRibxUse4STvbVJuYCofqsF0kn6tfXwKV9Yi/2oUF4MBg9bQKbNsPYIOx9HIonxdvPmf2gviHEXPZAcIwpxK5oDZPXfxiI4esK5joO8ezIBfvqB/zBEexCCe+FI9C1GlavXWArmOWEaFz7YtCIyZJIxbDC2tDk/ZFU3JSaTUBeSVvSc/pwDMj4kW1mIFYoC0lZydDqZ7KZlci+dXWLXnrXGzQ60gFAc8OcCKm/v5+3vvWtHDp0aHLbCy+8wJ133slnPvMZbrjhBu666y7e85738PDDDwPwxBNP8M1vfpNrr70WEA+nWkCD1CzS3KqqK03N6VsX6cWe4v5tTLstOXglcMZhZAAG++DUKRnYl0yJhc6pfhjog9IYeMWz7kZ9/AaQtNkpwtTRKGF6SuXZ83Gu1Dy1QDhW3kIiJhdJJfYhkUl7cDzjwbFrvWmrN0Gi2E+hdx8Vz8EzDdY1WniFEcZGTzJWGcBxC3QQpv+0AXVt8NMPjkP3ewKJHE8Ex6Au8RmmiixUmp4lLCefL3SZ8wlSpsk0dlMrZi4DaXuJflgXG5qYn+4FYkR+jyIqbAh+n2wuja5f0RSZysgJ037GtAGZPpKe863wcaYpKjs/yPGYQR/SJAHCVNlPva5YERmUX0XO9TwT0m233cZtt93GI488Mrlt79693HfffbzjHe8A4P3vfz9veMMbAHAch927d3P99dfT0FC7MXgG4ViCPJJSakaumkeRK1uH0IJFW3NaCO2EdBFpZKnmLf9f9v48zrLrqvNEv/ucc4cYMyLnQakhJVmW5UG2kREGW0VhMFB22a7X0C7rlQu/pvCjqu0H/jSv2w968Ke7LT7VfODDgy7coqqfi6IKmppwFZSwoWyMGSwP2JJlpWXJmjJTOcYccadz9t7vj7XX3fvejMjMiIxMZWTGys/NuPfcc8+wzznrt9dav7XWioDSqW+Lyb/nsGRm12vQ6UqV60YDMj3bpVW3chp4OrxOILfVrUil79nwK08c0yshvfCqiPGiWxHLdonYoE+PRR0qbSRhdxb4STxZtcA357/CMQRQfuVLX6eL58+851HE4nh/2M4TYZsN4H8LfzvAtxDwe4FIdddJzSgRIFU9FWFfCwgJYQKxli5XCsQi5RWvhB/92/CD3wt33MZ2/tFGRLWATT7rVV1tPFezPrTz6WrfreUyW6WGW65+AOhPlXPoK2+THldqIV3r112fyNCtzHe54oD08MMPc+TIEX7mZ36mv+wd73jHwDpPPfUUd9xxBwCPP/443nvuvfdeTpw4wQMPPMDDDz/MzTffvO4DVWkyWBIIZAh0Zqu3nEve69woJTjo+y1JaFDxDrqzMH8Cnj8KzVGo1YMJsQLLi8EvvbbsQMrx3Ik8UgvEojgzxOZ4i4gi36wcpNVELRaHAKVaKmXY/5MI+ECkpC8g1/5fhd+ew/djPv+b95QIsLxEdLt1kYrhM8g99E+JwHI2bPsIAjB1BIgc4iq0YZ0msQXGKDG2NLpJY9G/Jzs9ODMn7Mrs2qasXxuiT36ayaZPPcQ7TB34cL7PRNcbjtGsZVGttWx4FY0zJW4/byTmmR6LSfe7Vfw4qmXDPep8qHZRSkWJS5R1AdKRI0cu+H2v1+OXfumX+Nmf/VlArKd77rmHX/u1X2P37t18+MMf5oMf/CCPPPLImtvodrt0uzFKsbg4SDZWlpPOTiEkEDLotRzOp1EFloJRmhEP1/4lP1+8xEUWT8Gxp6E+Ehg7DehVsNION8PaimwHMqu/AxmTp4glfOaI7jxlLEJ0eKwm6mXwG9CdKSH7XNhvqgaeRejnq8mLqyw7Rpy3EbZ1Fjn2XlieAf8y7EMnNDsQq3sXEjubRsBqBRmPOQJvBLnvFJCmiAzXy5nopENnWh04PSt0dh8C3Gbr3alXXsKoeX3SVen3iE996gbzRCtpLYU/7JK73HE3nOfQNamGIjkenU5fC8y5C0lS6QIXHnwfFK6TQtAXmRSnsqneql/4hV9gfHycn/qpnwLgwQcf5MEHH+x//+u//uscOXKExcVFJicnV93GQw89xMc+9rE196GXbS+xa+gy0drRyzgRvldHVYYoFf2rYcI08XPLSncRzn5TmHZZDTpOZtS9DlTnEMheWwziHtL422nEIlErJUeYbfsRlt08otitEUZnz8Xb8sB+GBuD556D6jLoeJpnfjewK4MDDfjrEr6+Dr9hi0Hl7oh1z1UNGMTyIll3FPgPxOoe+5F7xBGtoCkklqR/VZTG3mRjvbXOw/FzC/C1p+D4LNzahunpDWz1RpGeFBy2XckNMoEwoIw5k4KQJo2o83UtuRoMtuGo47UMQKuJWqXBD+U3/uBvGiD98R//MZ/4xCf44he/SG2N2kVTU1M45zh58uSagPTRj36Uj3zkI/3Pi4uLHD58uP85Ncj1sqUkhtTigdUN3mFDfWuLl9bd3UUJjpo69DKoulClWTRr/hqHxI9eRBS2NipcCOuME2sjH5yC8ULKafUMlB4WVuT5n5iS8nrjE4KJzoVUjVyIb6e+IylCIyOyvkeauuY+qIZRSZ+qTwgZadRJa8GdBRzaAafOwumzcLa6tHjWamc9/KgokUEBUF+LDPKnamEkh0tPrebd1yhBkqsODKYxDjt/Ums+dSAxOSZVvsfCZGNbVhcfpqPeCq3flQGQgoI0jn4XPaBfCWFN6+hi32+WrLZ9Bc5r1UU77H+qwPeEzOCVoDGsiS9NNgWQnn32WR588EF+4zd+g1e96lX95R/5yEe4//77+4SHL3/5y2RZNgAww9JoNGg01p5bVoiF0yKSG9LMfr20HeSS7iD2QUotIXW1bHnryAWac9VBRqIOjZ0ITF886uMQRft/Ao8iJIExIg1+BGEx1hBG6ne9AdwUzI/AkoOVEr7zjLQfeuPfgOnDklx+65cEjKYPiuHWWoR/+XPSLeP222HnAeha+Pf/CcYrscCmboXRA7DnPthRwVTw3NabsOsWuPc/w77PwqeWYXaTk6GmECLFTQg4al0LZdtlyL2mqgLkXhy2V5QdOEPM2dLHcj9yP5bJuqmDRiuO6L1aAHzXq+Fn/2t43Wtgx7Z1dEFRMLJV4ibSqYGTC5gRGHOXIi+nZrjWtZKjT2CgK8Bfar8uuCq079Wk3W7zjne8g3e/+928613vYnl5GYCxsTHuvfdefv7nf579+/dTVRUf+tCH+Imf+AlGRzce+tXcky7R6M6IM1yd16TVGbQ6tBqVZfL++pLguKwWAvXy4jeE0uGPIHGbFxjMr5kycHMNdh2Cqf1Q7IVOLgZY1pQKPYdug+m9sGcv7NwDjUnovlFYryOTUg2luRO+/x9IQvrohIS7qi68ZQUaDsZzqN0EZlxa0FQj0N4tk1ozBrU7YboLxRTc8204NQsvHZPQykbrpWo6QIkAyz0IIKkVo8TVCQQkDhA7EoNMaO4kdh9OnT/6OZ0jaqUJg9y3WhZTk2H1BXI/jwG025izZ8QXvx07WkWSmbohJqCWnZAAnczmiwBMw9nzq8rLPdZpZPvlPhaI9v4QUHrEM+PCS9ezlbhInF2X7/6yAenTn/40R48e5ejRo/zmb/5mf/lzzz3H+9//fo4ePcq73vUuJiYmeM973sPHP/7xDe9LXXIKKgo6ehL6cEMkMeg6SgFPvZ3DRuWWZtwB/TOzakpfXBTAb0Nm9V8kjuFuYI+B/TXYuQ8m74SVOnR70GuBb4jrbfc+AaLJCfEwNcdg/50SXzKFFCjPGvD6H5VUqXYlIGJb8JqlkHdeAzcJZQZnl8CNQndKih+7cWnpNJnD+B54xTiMn5B+ZAszsFzG65hd8pkPAtIYUiLpUFh2ColNtpCJzg4ErNSR0kKA6RCDkQg9DrXKU1kMY9wglhvU36UsUL1HR4Cs08Gcm5GY4LasIT7EhxDQzrKgCJMxM0YUpPHXeOklvYNSksNwEOJqSkpaCC8/9FkZdS7cud4FkHLynb3CgOQTCtW73/3ugc/D8tBDD/HQQw9tZDfn7xeJZ4wQe+PojFV9+Vr2RpWDVmWoJe8nk/U7yKW/9ALpW0HWp7wy4MeABxAr4dPAZ5F4knFQtKB2HEZ74qabacELJ6B+G9QPwJ49Aij2ODz3pHRR3vNWKbh98gQ89xS02/Cmd8FSW4pLzJwEHLz6NXIAXQdjZ6FRgp2GvCmJ7jt3QmMMMHDwLth3H9zzdmi34PRJ+NT/Dn/6r6XAd4FYOjMImFyK6PxzCaGHn0Puq5sRivkpYvbJKIM5UA4hMCgxZoQLO1rSlvCGQRdzl8hkVFVkgdGlRSaff0ZOeFvWEI31hMThwknFkjwknZpM3hd1qYZgtGDptZjbo1MTOB98Xq7jTRNoQv9on1hFqf63jn7x2A3IlswJ9UMvFX3QNRit8SFtZaAzU2XgqWtGZ6arZSPcKDKBnPPrkPjJHEIFnwSmPdSXYTGDEw7OduDUCtRnQt+eLiwsSAfxRg1qDXjpGzC/DM8fgxPPi1U1eQBaAUgWzoqemNgh92/ZhpEZqDuY2CPuvqoGnTkwUzC5G8qupFf1OtBtC6Hq0C3wxu8V111hYbyEZ08IU7qRB1euhxMlLAyZTpqNspfYHLCNPH5NBCR0HYOAj77XdiYKJDrBUaLDarJaQwAtldQlFnPNgDwz5BPjZJmHs6fFJO37A260u/NCko5FFtyawUryGf1q3VrcVKcX/SKq65HVPA+bzcJTrZbSXfRvxvlabzOpWSlP2dAvEEtwQTgfyAsBdPo5R8EqUq+e84OHtA5X85YDJFUAGvhV99vwLaE10EYRZbMLCc7vQGaqqjwUkJRplSbc3kjikDH9HmScvgexNnRMHp2Hr81LK4pz4WVOAiflemgb8gd+AG69Fb74f8DxGfjWmbiPxz43uE9Th2eeBjsHvVOybGoE3vkaqciPh/keTByGA6+SakjPzsHZ4wJ+rbPwmjfBj/4YFCvgO2Dn4XP/Ap78MzgwJsd0yMJvz8NXQkJSGnatAW9E7pFxxLWr1cdVtEbdc8QKH/vCbxaIVnqZjMOlSgu59xbDfufC9seKGtN3HiEvcvj2UWidC2uvzk7dFoh1Pgjld3S52riXaxUpBWh4n+vor3DJkh6n5imlQQuIU6PNAiRHzM7TBJkSfCtJ4AyxIWtl9lnPhczgfNyEY1CJrqP24pbTvWrMDgeB64iScMkyTyxL0yHWI+sgM2CNN6iC2nKDsYmisZR5RAEfIpJBziEKsxe+19yudJ5WIsr5y0/C0WMwcxpWLlKF1VfQe0kmXSqtHvzVc8kj5qB2Dr7+v0pIoOxBdwXGmvDq2+D4Y7D4goCab4NfhKlpeOA9MFrCqIXpEn7s6/DAcbGs5pE+T98I5/NtBAQmkHtISQvjSPPdA8RadQpIu8M4uTBGWjUiaZfWJ9sMTBaHxmCEWB+vCvs/ASxWFf75Y5g3vAp++K2w7xDnR6W2ZVAsgxFiQnVutYpyYmme9SjxYBn54KrygDfiBjQpSKVU8cu1mmxyjAlZABispJi+3ygwqdWl0Xnk/JQ9Z8uQ5OqgVwYmowXTBOohVqc5SEpqCNaptfyfv/MHl3wkW04Hp6QEvUw6R5lAhrPNYF8dLUGTApK69PSy67rXKvP/Skp6Ky+H9xMIEHWRWEqLaEmuhjNKAn0uWE2XJA7swuCinoVnzg6tN8N5pRj27pJ+hC8twEseeiH/t9aCe98Mt74C3CI0Kpi2cP9J8MvQMnCqhCdLAaUZJE40QgQldYqNIIA0RQSn0fB+KhzHPLEgbCuMzS6iG2/4fhpWGXUG59cesc7azsG5WekS+8Z7YHKaKzMTv15kyJFvAEySb6QFSy9UmWGt7epfF11TzgRyhA+gFPbXp8oMbz+lvKznfMJ7rxFGPa/kmC4bjKAfF0rjz74nKSW2lNhQZcVvbq1YSnkYA1cKcJEwQX3RJzv8+aNfv+Sj2XKApL577ZszicQAxhBluhNhjGk1Z+2+uYhcsgVEqe5HXFMQb6PUUrqRvfTL4XUWGa8XiOC0/syCKyPn5uDffzp+9g72NOB79sBf/wl89TNwZhEOjsH3H4bvuQ9ufQf4E9A7Cm//Eymw+jTiKmsj4DJBdO+u9nCUYX11mqQg3UHASic+amnpHHq1GNJqcgcxr452T06kd6VK214vok9xLtROfaAznXrWiEki6xGdpoYEW2sTA6yC3EKh9UzSVOqUJjXsfks5oatNhXWqPMQB9mH6nMbCBkoMbUSURLEM9IQun2Xy6gYw6q4IIFkL3ZZYRL022HHojUBWBVCuQqNCPX41Hy4diLccIKXeVP2srrs0JqQuT0sEmDRepOVd9LIq8UEN19WyAK53kHLEXJk5xDJaIVqdw6V4Xk5xTkr1pbLg4diS5DfZEubakFloLYm1lI1DeRJOzcHXGKyVp26zSSTOOJq8FFCUkamPmd4Pmlag9+I8kaigkpauGk32u9o9pQm4BmClBcdego7aqOt1N91IEsgKXt1zIMy7gggYl6q4g/XhghvKaezESVpFnyyhAX6IpYn0DlnNUhqyfAb+DhMZwvt+5QOTbE63kw39ZgNEDa/utioy5wzQ7eLLEt9pBTedw3dWAiB1MWRk3kFeybkbK0CWFrT01zkgac06BY42okAt8qBrzCOdzSvBQU+2TWynoOVctGjrKIOk6TS36XqXChm700Smnc7LV9j89uWbLfM9+Iszg8uaFpbb0DoKrWdg9svw6R58lDhZyREX3H7EOtkZfrsLiRW1kPmjVmtQy8cQK4X0wrZKpNrFaLJeM2wrEGa5lbXvq0Be7r/nzAx86TH4gbcjmVCbVU/8epMh11y/51ARl69LWYdpiCslybNTJd6toGSzPAJS34VWxN9eVHuk/gY9/tRaUi0XWGzak8mk36WZbBuZrITt9Ktc2OimW1yEXhe70uoTGaruCj4ku9arkqxeF0DSWf0wIPV7PV2abDlAUlGrSEFJ5wlpbV+dP6ilpNW9tdWAXjrNY9I4iFLCbxTXnUfiKKeAryLAM4tYEB0EjE6F5Zfeb/XaEH20z50Va+kJC98kWisZYhWlmSlNYhKstsTQ+0DzhLQSA8TOtvvD9y+F5SWxBt65sCxHLKgx1ubLDdxzyx04dg7aXQa7j27L+aJuu1oS19mgZeSHlbQVy8g5Kb6YI3kFWQ5mhEEwSIMAGuVea1+plQODVlXqB0oXpesM73e9krg69TiVRWct3jqstdiyxPZ62G4H7yqoLNaXFDYnKzwmM2R5RpYVZK4E53j08e/wK598hEe/9q1LPpotC0ip4Zu66IaJkTkRkNSI9kO/0XmMulzSVlxp2BCuX12grroTiPKcC39TF97Smr++dsV6aFVwpiX5UkddbEsBsfGeWjR6X40Tu+nq3FXvK0ecENaQuNM4wsbrIWOlDQfVVdwi5iel7NCLSq+U6rULS7C4BM3Q5joPimi7nNCQqIJVTZCxPkCC88gE3sdKBNYGPDBhNxrTUeDTq76a+w0GNUiqxYY1TOqKS+uPpAy+lNW3XvFDh5ccs0fO1cs5e++pqoqqLKnKHt5asBU+szgMhQOTZeR5jinaeBxUFcdeOM7/9R8/v66j2pKAlBGVwERY5hE3U5pHlCHg0qVfdnRVUYDS2bC68QrEVdMlFmIdpvFeL3ITEXxnESLDLJH6fREG9zUrJ9rwe8fhsJf75aiPLSh0onEz4lLbh1iCZ4mW8kj4q5MWvQdArJzXMpgUCwJMyrjT/SipoRH2ta7WFN7Dpz4NTzwF3/0mOHQAbj6UzMy35XxRRb2R8VGwLyB3wfW0ItRnDYk4A2M+wQMFJIgaosb5QJh+vtCxpUDjQnwqdT9ermhSTAd8m4vFeayr6NouVbeLqypsr0utyinKjKKALDPkeU7DWmqdDtZaytb6q4tsSUCC6ILTIqs6H9JwYppRr3ECJTVoqRf11g4RO/sJkxALZKa32/UoSupQFafcG53NXyvsuvVKDzgX9EiDGF+sIQCl+UU1ogWtHCEYvLeUaKDWEkTmnKoIzT1SgkyVLNMeSeuKS3a7MD8H33gCTp2EczPwfffDzQe5mBK5cSW9QrCxuErQBtbGmniZiTeBMaFmpE8UQ0r7Xo22cqnHMfQb0w/QsH5r70L7SI4vjR1VPXyvh+uV2LJHt9ui12tTdduU3Q6usriqwuQOsgznfB+Qskzyj6qqotpA1eMtCUga61EXioZ5UxKlWkqjxORDVTJagCXNZdLtKtApIGl/HLg+LSOISraGBPebDIJ7WlR+q4nGb84ly5RRdwipRjGBjMEiMbF1NfIuDIaeU5BOHS91ovW1gtxras1rhZFLvpdaK/I6cUw+53Xo/jT88NvCjsOet113QVJH+0bv2nDneydV1rvdWG0gnZ2W4Sr3tahSUjbLilFJ7e8U7DZhu+purLpQ9aQuV7sN7TZlu03Z67CyMkfZalG1O3RbXbyTO99nOS4QKoyBIpeBqCqLtRW93g0ESLPEyz6JPPCTxNtBwUYrNeiMWK0AnaWqW0YTI0fDsi05MBsUfXw7SP6puuo0v2argtFaoq5ciPdJHbn+h4htIfT+SpvyqTpQOvxXiZbPNJEIkSW/S++9y1ZTyn46flK2Vm/AvoNcv9Oll0OSaWpRBJcdcSZRy6AwYjGZNMdpvUm3lyIpwG62JAkvtUDd7gaiRp6T5zl2qOyPw2LD+LjSU1pPnmUYAz3Tw3lHr/L8T//sMxx94fS6j2jL6l1lyemQ5gw2PksrOiipQRXMsLdXt6PJthtJn9vqkrorIbrtrmWa90ZF57Gp6tBq3XuRPKT03hlmXRqiu3iGaEVr0jbELrP6UnC6/PvKw9wcPP0MTEzKa+/Bq4hHqfMyXQabr4wvVzaSkxP+EmJHxoiC1lPOkfpthUkWqAba7Hie7iN9Cn3yStdL/653HyERNg9glOWQZZg8I8syTDgODzjvcd6Br3DOgXH4PMcYgzGGsixZ6VR8/mvPcHJm/TSoLQdIChaaDZ+WC5pjsA+NY7D9hMaOVNmoYtKMgR2sM9h8nckoUmhUKdBn2NruutXEEJvfjRHZdWPEKh/TCEFG849s+M1+YgzoHPF+UjVgEWDayflVGTZVTf/xn8DXvgLv/wDc+3q481XrKmB5eaLBcFXGaeKj2oPXgmxkPIIj1odkWFsx0EZB/FLhYqb0p6tZ0imdNqqow3094hlIdTeIxddooLT3vNfD5T0KCnrOYCtPZS22KrFWO8x5bCHWVL1ep9ft0W51LtiS6EKy5QAJ5KBvCu+Vgjs8N0kz6pXtNMkgSy71NKeva2mOd7VEH6/DSJ7MObbozXEBMcikYwdSGmiKSOJQx4iyCvW+SIvBjCPA1UQmQFq5QS1sjT/22CxraA1pd+DsLHzxUal312zCHXfC7XdcqT1y/lMS6NB9QNLg+7UgG32Ch1xjJtwVJosKOwvVIGIm6GXs71IlodIMjPlQHlM/CXg921UtGTSmVmqwFlt2sb0uvW4Xbx0myyR+6hHrKPzGOrGgqsrivaPX691YgFQD7kJmr+qlVIN5mG3niMpjF4N9kIaHTH+jzLobCZi0pM0RJO/oLNfOXHezJEMqL+xGJjRKMFBxxIoMWgpIwahBBLNxhAABg3lKmjqQ5hxdESkref3RZ2DqUZg5BX/nx+DI7fL9FSM4+CTrPrEmIMnFGc6r2UqSTFGNEReWRZblSHXvPAeTciuvxnmmWXB6DVS7hXJSJuWArme7gUvbb7hXhrL6JVW3Ta/TottawXtPnuUBfj3Wqp/JY4wNQOmx1tHtdm8sQIIYE1L3nGJ9OmM1iILYQaza3EAUhibFqjsvR1x+K4jCaYb1t+JjtREpETLDXwBfAr4C/R5J10uvUo9c3zpiBWr/ogli8mqag6SMuanweZZoKY0SVYTmKikZYoKr+GAtr8BnvwAHD8Pdd8GRV8DIlSovFJ4an8zQtT21NrDq+yy2ogS71iSMtnpIEFUfjEm1y9UWKxOAqivvjZdCpiadjl+qaMAiSZIxCODWCmjUqY2M4J2lXm9QdluUvQ5kDhPysp0H7zyddgcM5HmB9z5YSBs7wy0HSKoE1LevFG+9HClTX0FKmVLq7y+JVcA1o16rNDhEuWzVvJvLkYpYMmiWCPrXk6hbrcNg0V19fjT3aoQYUtZ7Y4HYxE8f5xFipQdNL2hwFdVVVcHpM3DyJTh5HA4dhEYhs/hNt5RS30NwH2kF6jUrE1xpGd7f5ZxzFiyN9LOS/K+WNXQx0WuQkC4GrvN6LNRUY4ZrqJW+s5wsL8iLglqthqtyKgNZnpNhyUqDt0J0qGwlxSysY3a5y5n5FtZtTINuOUDSRM2/Jlo3+xmkbI8SSQs6b9NqA+pWWUaU0kT4zSLiytmBZOxfb+6qi4nm3EwQqxacRSyK60m0v6a62nR+2ETIDGkKgbYV/zaxxJTW3NbPR4jJ1y+rujpzHB7/K7htBxQ3wditV2hHHkwlSZRVGR60AmrjDKYIX03RKcVm7TudhqkWuRYkuOaKIlQcV/adTtNTjuylip5fLtcxzwcIMnmeMzk5SbdR0B1pSJJst01VdvDehZJ3FmstHWv55Gee5LOPHadbboyfu+UAqcFg0UutRaaApPEhlTSmpAVX0yoE6nzIie0HrpW50NUSPdecyDwb4foaA803O0ykaE+Ev+l9URArv6vnXjlUGQJSailpc71roorH8yfgc38FkxkcuRVe9z0wug9Gdm3iTlYhLjgHxiJtf32wMNJ8nCt5FyUVFTYNkLbCXa9WzbB1qGNxoXFIyRCaEBNaT9gSOj3olNJ/q1diSkctL/B5DfIKlxf4oka93sB7iRmBuO6qUpJhNwpGsAUBSZXJAjEDYAxRLmNElwycz6arGHQ66GeIsYEdV/wMrj1JjXxlko1wDSjZTZQRJBZ0G5Har4nUp8JnjSlqPppa1/oeYr+jHcS8o2tChT3zAjz7IuRn4DW3w/467HsDjOwMK1zOUaZ3yFD8xLsQTAjcROOIMJ2SHFLZrBHTacRwns41cUU2UYZzjlJASs/VcWnnruOmnV7LaPG2u9DtQbeEboWpLEVWg6zCZBVVVsPnlkajgbUVZSlWmfOOsixx7vJctlsOkBRYEqIic8RLoQpFSZnqfplEkh4dUtFaEyBfQ2yYtuUGYxPlGQTklUk2wfUFSFp5whPdk9pOYy9CetmH3C+aa6SPthbm1Xp4m5cGqfb7Jonz8IXn4OmzMD8D7/qv4G23ESkaGxWteZJYIs6GWV3wR1gLeZosupqslwV2MUkp0BAJ99fbkxwZbf1rkV+OK9Emr0BS8X7Ae9dnFVqD7azQKzv0bJd2b5lur0Or1WJ5eZlWq8XS4pK47ZzQvi9HttyVS72manR2iFW9lRml8SUNOitzTmfBY0isZG9YrgU3r7e51aWKdtFdQuJpS1xfxI5hMkOGnGcXyUnSOWda2UNdmMOtSvR1+XHGK0AEmG1BWcLjT8Mbj8PyDIzshixwSTdEdEgpx35wsVmLcaZP6FrbU9no8Sj9eaiioNnEsbzmJLlfzGp5X5fK/kvddsObMJAZTPjrM2ScvcfjcN7inLxs6JVU2YqVdo/TCx2W2uuvX5fKlgMk7VqtQ+kQRWqIRUFTpTGNgM5eYtxpPzEfZZoYD7hRwQhk7HrAZxDa919zfQGSVvP4DtECWg7fTYW/WqF7GRmLA8AtRDKMJlgbBMSGC6+uT9QmTx3HmyRLJfz5GbjnG3DXn8JrvhdGd4LZqENafQ8hO18rGQA0J0LtMxsSM9V+1ByX4e2kcjk2ZrDY+oF9E/KDrie7XmWouKpJexyEZf2MufWOqRHauLL1ihxsDlUoj2QNeZGT+5zcFxRFTmVz8kLe53mOwfD0ySV+5Q+exN5oLrshUuZ5laQ8onjUxaKumhUEgAwxKK2uvasRfr3WRctD6uO8VcBILZth1Qfx5lZ3rkXiRXq/qPtuGbkf1IpSiFA2po6FTlo2x/E07G7aRMkNjBVgDcw76BSSW3JFKtyEETF1YqHRNAljeCautufljqBWJ9jKibiXKsm5GUP05Zjzv7/gOCSU8eFqn9qqvQzlkvJM2HzOQqNGkXsooNWVFhN5JnXusiy0nDCGyl7+vbzlACkVreCkL+1x1CYSHDRzfgmh6GqOiBZRvZ5v4wuJH3qvgLTV5pg6IVlNtevNrXNoh9TnUxlB7pM2sWafuue0DuICEcDGiZOgy0+NVEC6ApIbmKzJyZyrYDmHWh58jH4dbrtLVTAZmEYCSPrb4Vp3Pll+uU9eKOezat7N9QxSaq1uRFJAsvGvr5JeSGE6luVQFBhvwRXkhSerQh5SlkkZIQWmPCPLNmestzQgab8ji8QGtGTLcEeSLhIvOEGkNWtmvsYUrmZ5xGtB1JJcRsBaq1drkvBWEc0JWk11akwxleG5ZBP4PmIOUjoep4kdXieJ7MMxrvEHp3RwugOf+mP4/FfhvWfh7lfBA98LU2Mwvh5nY1peV62S8DZ3odjocA19leFlafq5fr9RSanlwyw7tcKuR1kNkIZjaWtJyi8engxlkBXQyAWUqipZz2KrLmW3TbvVYnllmbnZOZyzSIWGnCzbnPG+pp+ri4mSQlTJ6GVJc416yfqLRGsg/d31OI+6mOhcSS3IGeAl4CQxtrIV5EJzeJ2Pp25ZGHT1qssPBkFZl6vV2EjeX5mHRu/InMvu0esRUJqZg/kleOwbQmq4/7vBuoHVVMzA0mH7eWiZD07QrBCrqF/UczXX0fDTNXw1NiLptlPX4I32JA9fwbXOP7VQ03LSqajr1UTLU+NKxoRfSOsJ5xzWVljrqKqK504vcWJmc1LotzQgqai7KZUWEZTSnjS6XDt46nc3qnhE/f1n4AngD1k9HrMVJSUhp83xGojF0wmfv4lQ3afC5xqx/uEB4A6ihXTljlTrjIwhjsVNqiBoK/jDR2BmAf7+g5fwA52mDJcqTmIOVU8IBMUomDRL62KiHNjNkpSnfKOJXhOdPl0sADFE9b7ohMdAVg/EBktWlRShaZ/8Leh2V5hfXOFXPvU4L83e4ICk8yxtL1Eln0GUrCqkHnKis0Smndawm0Ueud1X8divFUlJHwtcP1TvNHXQDP3dhTDkbkWILj3ENadUd83B0khHGhHRbW++eGLnrhyBRs0EU0BYJNYWWadkbciWgWVoleAWYbIOeR0BQAUe9S0oDWT4bFebVbOOmNRmj96NZhGlohOE9HPqHk1KAHgLtgvGQaa5R06qbChT0QcXXZ6BDdvR2y8DkxuyPKdWq1HUatTCqygKyYveJG7OuuPXMzMz3HbbbTz//PP9ZR/60If6HQONMdxxR+zL8sQTT3DfffcxPT3Nz/3cz224LPmwpPM37WGjBVN1HqB5J21kvrmAZNnPhb/zCCAtMsg9uZ4zGVTtqDdf67UtM9Cua0uLgpGSkNPPe4GbgXuAuxFgmkFA6RQRpGDQW3/lgVrddAUCSHvA7I+vy6mdkXfBtKBcgNYsLJwFN0+sVJgmUQwHvRlah4ss25YrI6u5UlP3W/p++CnvgWuBa4PrCaPOlwJSthRg8iFelLrsMh8eHi+Nc40RC6koqNUKikLAaTOnBeuykM6dO8c73/nOATAC+OpXv8of/uEf8uY3vxmQIBdAt9vlne98J29/+9v53d/9XT784Q/zyU9+kg984AOXddCpoa7Gulb01uKqKVtMS8GAANQpBITGiIHsHrGG2062sOl4CaLVvB8lWg4KzNeD6KOZXsP9CBi9Cbk3FoFnkfPWe6OLgLImUKs6Xs0lvLmSI1X2dkN2syiMLIfpI1Afh8Y4nP7n0P72xjZfejh1Gv7j78K9r4Q7b0ZsRYucbfq0uORvUtkbGFSIw99ty5WXlGASCQfnR9MhWrvaskLv6GSyUSXO+Urr2fWk6kZlodeTz90W3dYynZUVVlZWKMuS0VFxYHcrady3WbKuLb33ve/lve9978Cyqqp44okneOtb38rU1BRTU1NMTEwA8Mgjj7CwsMAv//Ivc/vtt/Pxj3+cf/bP/tllH3SK/2oJ9YgWkn4HMRCtRVeV6KAWVQeZJ6rVtMDVmA2/vKJuunmEefhtpLL3EteHelntHPQ+WSQSOBaQ66+TmbTMlPY4SksFXVmpCXU6G4ORvTB+CKZugh0HYXwf5DvDkW1gPuqBpWV47JvwwrMwcxJWljDdFsaXmL7rJ3XTJbEjH6oiWBsYWE6qTVt3fdwwW06Sa9KvVpFqRAWi4JbTeoPOkXaExYZraq18Z50AkQuApb93Hu88zjm888HL5Tl+bpnHnj1zWcVUh2VdhsDDDz/MkSNH+Jmf+Zn+sscffxzvPffeey8nTpzggQce4OGHH+bmm2/mscce4/7772d0VOagr33ta3nyyScvuI9ut0u32+1/Xlxcfd4eWlThEKXiiV72tDrzLgaNVz1pvWw9BJD0dw3gINc3DVwtgS7wdeCLCLuue4HfbHU5gwDRceLccT9iJeeIVXwYyVWbRuJM+5D7Ry3uKyvBXZLXYe8rYcce2HtQlEOvDcUdyHTqS2wojnRmFv6vR6A3D+WsnPSeCg7uZhB2le6bUom9xBna7QBIhQS7axk0txHp6onmdWnMx4b5gzqXhwvNEgHHanUNXcfJsv56VQSqAXetWF8mEyJDXuRUtqLb6/Gv//Sb/M5nv7Fp8SNYJyAdOXLkvGVHjx7lnnvu4dd+7dfYvXs3H/7wh/ngBz/II488wuLiIrfddlt/XRN8kHNzc0xPT6+6j4ceeoiPfexjl3Q8CihplkQPmfmqFbWEKJqbiPEB5S8ZxDLqIEqnEV5PEQFJyaRa5SHdn4aDdVs5Eoq+1kOtafpiiYzLFUrRvOqSFlBJnRiqZtWC1ms4ijDpRpBrrOWDFLSXEYC6sqJ7DFOmegYjheQLZR58AWN7YGUGyhqDPoB17MIC334Oshbs2i2VHA7eTiTHQ3TirhY/0r823jB1K/lIN3ytk6skffdQJlaS0euUPMFZsG6qCsqegI3VGGW4EXywiPSa6qXNs2A5JcvQ+FFGnuXkmfzFmE0FI9iEUMmDDz7Igw9GOumv//qvc+TIERYXFymKgkajMbB+s9mk1WqtCUgf/ehH+chHPtL/vLi4yOHDh1dd1xPZdVq1wRMTZLPwfgxpOzAfPs8iIKSlYfRSafO/Y8THUz20hL9tIgdpZ7KeBtCHQYpV3r9conPelHmmxWmvh3lueh8MWzRaEmi49GOFWMRNYkuJjEj40DSBKy8KSD7MGDIYrYklkhcwOgWNqQBIG0xf9sCJ01DNw/33wS6tf68jx9D7tTbiEwZQYHFRXBs3+Y0i3ggoDbjrgphKQKgqIyANIEzi8oNAkQvfabFcp5lHAB5jTKjQYEKVBiGwbbZseux+amoK5xwnT55k586dPPHEEwPfLy0tUa+v7RBrNBrngdjFRDvGThAViQnvW0h85Hni7LhOVExKG3ecr8g0xuQQIFJauVoY3eQ3GvhOWVkTDOa/vNwyjxA6KmIrhvT8t7roBCWZxw/Up0vP0SCFU3cTk10biLXUQFpTtBE38JUHJAfMghuB3hkoj4jrxFdST2y0gFunYWQfPHEA3DlZfyMyvQtu2gf77oIdt3N+mVh1/6yWzU8SK/dSbqbXlll6MbGx49mWDUqIiA/zSky4QC7E+5wP16yKdNM8mXD4KrjpwpOjrjusEBrKHra7gquuzrTssukRH/nIR/i93/u9/ucvf/nLZFnG4cOHue+++/jiF7/Y/+7555+n2+2yc+fOy93tgOg4q8JRT3gHsYBmEVrvUliWsvRUYbWJlHGliitJQpuyzSPuwAVEUa0krx6RWKGvLoOVIl4u0flPiYzHGSSW8i1kXK43rtRQaLd/fgaxYHcgsaG01raCdFqpQUtKXfnafgZxGjaQev/hLGwbqiUo58DPI3ddh8tKXW5XMN+BU7MwuwjOrHLx1Y0TvnNelJtNLaOQfFKFmfh5NPFt2TwJLjgfmHAuWKVq4RgGPabeE5OD1PIxybqZtCnvGzipSzZ5ebG8vPOhGV+JtZa5xRaff+x5TpzdfF7uZVtI9957Lz//8z/P/v37qaqKD33oQ/zET/wEo6OjvPWtb2VhYYHf+q3f4v3vfz+/+Iu/yNve9rY+LXyzJAUfbVU9T7SQCN+rJTVCtIQWiXNBzXceHmaDBMRTKYiE2fRslhl02fW4/PZomyUlAspPIA35fovzXVjXgyjIpIAEcp0OIJbrNLFU0mFkUnGKWMVBuxDv5WoQGjLgEGKtjEtJnsxC+yxUHeguwfw3YelF8Ce4LJvt+DycXoY9fyno+8APQVFLbmIduTBt84EC3G71u08M4GGvK+s09anaSqV5t4poMms3Eg+cFdApVondreYOyPOYV1TkAYNWefq1DP7QLdbt9mitrNDtdjn6/Gk+/Kt/iNvsABKbAEjvf//7OXr0KO9617uYmJjgPe95Dx//+Mdl40XBww8/zPve9z5+7ud+Dmstn//85y/7oIelQ3xOUgZZ+v0cMtbLyExYwUQZdiWxQVuqgLSxX5NIAND1zyEAprlPytLTumcap9KYljoqXw5wUjbiEmIVneb6sopgkMCgVlF6jjXgVcQ6CCATDS2+ewYBqjoSH2xwfqH/KyNKvxmXPZ2bg5aFhRo0Sxhrywx3pBGzwS9HKgtf/xZM7YbFczCRQ65ucsNAooRJ2hVmIV7hoR+FbDahrt3HricwSmnUWgVhuAbIy3FMw5IHiyfMKHxixQIUGhNKEKbqiXWVW2L7jnDt0rSmnFWN3iupNzYESMPVFh566CEeeuihVdd997vfzdNPP81XvvIV3vzmN7Nnz56N7PKCohO3NCcJohtPc49ayToTCIi0k9+o8tEWFYbYvG+c6JZTB4VaZBqvcsTk3CbRuaI107RqwNWSJIRJi1ixYpZYMul6kVQ9rOY4qiHX+yDishtDrCOtEq+5aCuIhaRxwoKroXo8sQmGg8Vlca31chhzUNoQoNyMtg2I0jq3ALML0OuEGIKKKlybuHlCRe/Mx1VUOdfrAZA26dheVvFD7y14fdoLBju0piSQqyjnzY5MdMFBcKWGlYyRViTGQ5X8SCngWWDpKSj5mIPmjQKyfGd0k8O732S5KgUJDh06xKFDh67Y9lXhazg2va1KxO1yKwJCDpkVn01+m5Ah6RFnxk0GlVuOKDWNGU0Sy2HqxHU3MQFTc6XU899BlOHVLOaqx/IZosr7TnhdTx7/i4HrG5AcI4NMJFaQ6zCBsCpnkXtiP4O17LSJ45UVj0QmJ4BWMPFy6PbAjcHkFCyW0OmxKTzbeh3e9+Nw//2w75XiIjxPkoJLuYNGD3qFJErm0FdLmjB7XZQ2SYkcjtgh14FR0yFtbnOVsxVTNOgHzItQdT20JDUOGg3wYYJgiPeM1rLrFQJKvcDAc2Ug0YQ4VdWDqgutJbAVBV6o3nlBXhRkmxxySeW6uI3SkJzeNiMMNlJTN54WVNXlCRN/ID1Qv09dbikhVvelVGKt/KBgtUi01NJb+GrPIZXOPovESE4isZNFri8L6WKyiJz/OWJVhtNIrHEFmViMI5OXQwgwTRGv+5WVDJnKTNFvP+G74Geh04T5FejNQW9pc3bnHJw8DSfPSHuK8XFoDjNb07NWYDJJ3gshDybMxPt300ZHSx2sJvnMKu/TY9rsK5NEHr0VFqFWQ8j16ffRXLiaYpD9eyPWah4sm6wmn8nk2ngEpHx6fF7Oxzhh2OWJ1jNDJAYXqjNYee+tpbSWqqooq4ovPPYcX/3W8T4hfLPlugCkVNTju4c+b0nSLxDlo8y3tDRMmkukLjsNzy4TY1SN8DvCNjV/R29Phyj8HhIRIGxnnKgEr7aXXQFpDvgK8OmrvP9rRY6GFwjD7ghC7JgLy/Yj98zrgHuB28NyjR5cWSmAVyMWUpNowz0tb5d1Jn6ZfZJUqgoe+SwsdOCHfgSO3AbNC7jSfQ62DnkFRVBEqrjyPLiLlBa00dmzKsnVourncZuRJ2qzRWNGKwJCFfE8m1ncdT/r8GpPL7PgNqwHza3BHj0UddNpDDBJQvEhXqSGXQWU7SHyghf3sPX9IbfOsdJu0+12abXafPy3Pse3j53lSsl1B0gQKc5qmbSR8Z8Lf1Nju0sMX+5Ggtl7w3crxFhCPay7Eran9PC0wIZP9qHsPiVO6K07nhzX5d7OetxqoaUlbvTx1WD9s4h1sC0yWfgOqzciXEImLlrvcBkBqjpxrCuiBb5xKYgVvBvEyFaDOK0aCUdwlni1N2tmWsHx4/DP/zX82I/Afa+DsakYHE8ly6HRTALg0C9fk9XjDH1TFHR6jrrN1c5ZG8wMx3U2cgw6tkFxY4OVQHR3WRvwNk0iuFqi4xtA0AxPkfRYgn/HQLQ4g5u3aMZVc2QbtQ5kOp2+Nvi21yUggQyvXsYusc2CI5aHgciQ1Lmnlp5RUAme2f58SG9dGCRPaIVoLcOjLD8b9qseea0mcbnzO31slZDRJibhpsVmZxGQ1ArnN5KkDLmUCasuzFQccs0WwncLyPhpZEcfWQWqdNsbU02FFFI1NTCjkO8A14RKs+MceKXdtFjdMlrNvXWp4mF+Hr70VfiuV8Idh2Bkcg1ASoLmfdEaKZtZenaYF6mjO7zv1DmfrruR/RHyeTR+pDk/LpltGgEogrvL+6tsHOn5X2yc1xgn40OMSd10gaiRF4Hc4MQ9uUrlBWNMPNV+a4orJ9ctIM0hJzdJZFJ54iXT+YbGgRaB58LvjhEZj82wXlp65mDYroJKLXyeQBRYWqJG2ZOLYVsLiJV0C5d3bTWJUysJVEQSxteAx5EusG8K5/pFtlZr8suVHHgNMiY5kgg8H77TgrqpzCLjMwU8HV46CWnTpxv0x/1uxKYZ3+gBNsZg6g5o7ofGLjh0L8yX8Ows2N1Se6zdDEdyllhnJJWRcIQb7Na5NAuP/zn8wQ6YPQcf/H/C5I5L/PFmd2pVUIA4fUv3daUk8Gb9UrQ8rA0VzZPV1LVCBrm/dsqvXFDUtanTauUYqztPp+xBNNEZLxVCbIHxVtbOMvI8v+I4fN0Cks6h0pYU6fK0GZ0mUuoyzdDXwcmJ1HJNtk2JCuoyqxGZeh1i6wstZaQ10tICnxu9wAUxvqEW0rmw7GjyUuuuxfXTmhwi4/FC1TA032w3AkZtRHWn41BHgAtiTyyLMDFHwuc5ooWtFTjOMdg8+tKZkxlkB2D0IBy8W1pMjO+Gm14J59pQnZRk024LXjwOTqOQS5zvVkkdh71Vvl9d+la+92RViakbGK+vOkNeWy5HNV3IElrt/Wq/z1bZzkaJFWFEXPA7VD4E9ZMIca706lrYvLvA8V1Losen46XUKiOg0ydvKIMyD5UghNDhncM6z2PPvMRXnzrGwkpnzT1thly3gARyywzPHdXNtRBeaTHUpfCqI4poghhjUq++JfYN0i72GdFSyYkJqFPhu7RcUZVsBzY+0VJGoMa0FhEluQT8JYOgdD1KDWkPMcPqtoND7IomUp3hHGIBKZCojAH/RViWhmrniFGcs8hYK/hp9Y2lsHwH8UG6uHoqoHiNWEd3vQkO3wI798DB2+D0IjRegPYMLM7Aia+AyxEahk6tUpkgpmIvrPL92qIRmDrg9+3A3LYv5DldDUkzN1PgGa7RvpbouumTpE/pZRArXCnKuSQE9vWJNlLsNq9B3gyWlDr4t4L0A0fh1ZXJR60W3HnBDemQlIBSO8tavHWUzvIf/+JJ/r+/92dX/Eiva0BaS1Ll1U7e62VLC6qmv9FbfTKsqyHoBrEmnCEax44IeFmybg1RjiNE1t5GRI9Xje5vI+SFP2PDpTevecmAH0TIJ02EnHAMccml6tgzSOiYJbYZSV2cEwgzchdwJ5GJqWkCZ4lVPvT658j1LhGK+DwyMdjFpVhKDqpFsC3ZUrMJYyOiIHZNwH13wtxhmF2GWQunPg8nvszq3apaRE7npXez0rHRJo2Hv/J1dlYW3vRDMDJ2ydvZuKTkg+EZ/GZt71IlWAO2F2v1rXkYCnpDrq4tI6oxQiTblOGmdvLemGAhFeAakFeYAur1+qaXe1tLbkhASmW1HPWQLz/wiKiLTud2qQtQY1AKYKq02kR3X5MIakqSXc/jp5adbl9n6ucQpXIGiYE9jVDcr9dmewYp77MLGROl9q+mhrS9yBlimxFly2UIODWRa5dW7lCVo4QU9binNbE1sXaZWNFh+lJPwrXBdoVia8LsuwCadZhuwsiEtC3ffSssf521Y0Q9YsGs9TlklcQxC+w5cQrXbGJ6HYy3gTV3JWUtV9dG3V+rER8uJilrMbjrvB96KM3g6rquZ53uzWtJUipOAHKTuD+zTIgtmVDMTYgdZVfpfG94QEpFc7PPIcpnJzG+tIsYe9Jk2WNEhl7qzZ5CqMKt8J2WE1JRK2s9g++QmX6OxESeQADo08hs/SWESaeK93oVC/whfcdD3xJdKztnEXiMSGLZRZxcaC7aSaIrVaMGI0RW3X7keu1GLCa1hiskt2svMaH24uKAGeicgZNn4fAh2D0Ne3MYy8Sk3pfB2Ry+ADFKtZpjciH8XZ9lYYhFY88B7vhpep2KxtJLAoj1XVtY4V6q6JQuFb2TCjn/Io+LbCUUaV8hVRsuJ+fq5RIF4cAV9V1wXSng67uQVdDIwWdBiTgwjrwoMOexLK+MbAPSKqLWyAoxdJmCjpJwLTFHKXXn7UU61CopdhqJM0wjYDXG+qo2KEniWaK19SjionsCcdgsEAu/Xu+iluil2AR6DbQj8BhyzXSS0CD2rUo5XpqeCXI9dyPsSuW19cJ21XXokteFQ90ZZHuhuRd27QivCdhrYNTIDsaBRgE3H4S5gwgkniEWyVLZeE6S3ifjQKNWI282oBiDrHmhn11jklpG6yUZ6FVWf4guG1K8WS5AlGlSqloTWymGpJJaRhn9KhtZsJQ8UJahdFCJtSWnz87zb/7zV/naU8euyhFuA9IaUiFKXt01HeKtuohYIj0EaCaJdc+OIGB0B9F1p8pvFLGc0hykS1EpHcQt9wTiYmkhlO6jl/j7603W45wqEKU7xWDMbwzR/VrZu8aglaU5YwVi+dyEXNsJ5JouJd+pdaUvBbPVVWMOxa0wfos0yju4Bw7ugpuyeEAA0wW88nY4fQfS73iJ8wFp46KuyR3AyFiT2vQOaE5DLaX5XEkZvnM3sk9VrmmVh0sVvcKaNg8xNqR+EUIlirDdLBN3Zr/w6FaTFIwCIPfdc7lYf90udDtQdqh6Jd85fob/5lf/HZW9Oue7DUgXkJRdd4rYVRQiSUFv5TEEcAyiNmaAu8KyeQTA5sP21AGjl/gmIjX7ZPjtTWE73ya6Cv88fH8KCbTfiGB0qZIBNyPXZRLR9QUyiagjwKJuPA3zppZuA5k8HAJegQCaQSYW00R+2yFimxF1315YLVoovwNzHp7aCbsmoenh3kOSqKh62Xbgxb+AmT9DnI6bm0U2HrY4g4CSHP1i+KR38pWW1DK5HNnI75UIUReAKXKwQVFr88F+FXQjlSoyTQjZqqQGFaXe6FSqElq7zqbyDPKcvMivGplBZRuQLiLqpusQBytL3itvRRWbVniYI7LulLXVIrLsNK/JEeNJc0irdW3BtoKoIq388AKxl9G2RAeLulRVlL14AFG8kwxW2EjbSmTJK0/ee2IdQn0kNe9olDghGSNa0HAp82aHFE2dgNOn4exZ2DsJ1cHodQJRks0OjFUwlcNytqmJZKpOO0BpHVVZkbeXMN0WNEY3b0fn5Ruly/SEN5o/dDmi+wp+DBNcc2RiMThHLBrroxUxcMdsVTH0i69qq4qwWF7iyjPhdTVlG5DWIRUyq9xBdMGlCk8TJ08jt+sY4g46ScxxWg6/7RL7Ks2F988A30RiRTchgPR1ooW1Xmbe9SyaGKsNFFPa96sR6/R2ohfsO8RafoZYj66WLFNmnSZU60RkAbnmNyMPzB7EQiqS36WTjAuLk6NdrODoBExMSHb82+6SpEs1wcdH4O/+MNy9E/bV4DO/Dy+9wGZFCVvE3LWRhWWMOcv0k98idwXcvnsTsWG1eEufKUC8o3WasBHZiMtOJbjuTAPpD6SbVECCfvVvsms/D/aSJFByypZYgi9LodjVZRuQNiCaLHsWuYyTxNJCXURZ7UZiPAXiYptHgKcXvteE1i4SLO8C30AspJNIrKgX/moTwW2Joiy51Tq66oRAmY7LxFwkBSjtGJzUQ+6nVUJsrDiPAJ9HJhcTDLY2gagGLz0bJtR28AW0erDQhkUvB6SmeGZgTwF3HwZ+QLLnn/4WfOmPhfF1nmjEcjU2XhTlls0hgFQBxa2HGDlyGHNoP0yNEW35y1EP6ltIgSeN+bjk80Zo26msh8ywym8NDJIVVpNhq+5y9/tyShjzLJMEWBteZRd6XVyvi+10+e0//CJf/uZzV6RV+VqyDUgbEG2210Zu0x3E1MRFJK6wE6knt4wA0kx4jSHU4zmixXQkbOtRRGkuXr1T2bKigJSyFdXB8iLCSbsTUYXPh2WLiOU5RrRylUOgzEqNnmhB3ipZtsIgCKooFFyahEiUGYGsDp0KFtsw78CGWbnWLLod2HEYbrkJmjvh8cfga59noB11Pz9GM6XUxltdtGL9OSKLtH7nzYy+6XVw6y0wtQPMCgPtC1bzuq0p6couWBYpAIUKCybQqwccrxuRzQCFFChX2ZbR//zQOhemr1y7ooSGTO4l1xLqd9mFbhff7VK22/zvv/c5vnr0xat6ZNuAdBmiCbEvEisBqAvueWIL87Qw62eRQU9qC3M0/F3m+qo3dyWlhwD9AQTg1ZLsJN//BaIudFw16VkLrmrdwRwBJmVcg0wWdPkkMsHYx+VV1hAJdRK006rfBe198PufEzDYvVv+7hqBW6agbgQ9X383TEzCZ34MOmqjl9Ceh6f/FKkMvpN4F64u2qJkjliXb+5LT9A4dprdb/9B8iOvgIN3nZ8cuy69qy60EDT3VsAWhLVmLgeANls0ehhKLRsDRSbXhx79jqyZgicMZr4pyG4lCWrfNKCoIAvdgG0BRUFmrRRSfRly0bYB6TLEE7vDKg0cYjXp1WI+M1ft6K5v8UQrpsf54+wRK0DVotY8biBKeYqohtKEWFWVBbEXlr5XQsrlP6ZaA2IGOvOwuABPPQ87pmB/B3a2YXkSZncIehoDZgLG9sJdb4R2B6oSWgswf1IUi7+0+h9KdlbL0gIrC0sseM/OkyfJp/fAgSTIfZ6kNUqGcn/6rp1wHN6KYndWwLcfNPeRPm0ufLxXXkx8meSvI1C+CwGlgfNUMAo1PQwMjgtsLulhXSbqRbYTuMMmYRBqe5HkZfL1ZEpunmwD0mWKuu9SUSW4LVdelAa/GiAtJZ+nEfvhVsRt92qi22qOfn5+3yW3izh3VksKIiX88qUHzMCxJ+H4AuR7pI7cjknYuRf27oWdh2E8T0rGT8NP/rQA2Llz8LWvwHNNMAcRG/GFVUZidVF10yW0tK8qDv/149TyBtz5akmSzetr/FpHpEsky0O/lxAE2nQFZRVaGoQd5laUXp6HWPr6HJ5XTUwGhU5B1KcRxOsUJtTSN8ENORCJXGvsNiKXQ9pIRfsVLBCnceG8slyqfzfC3V3ZwDq8urINSNuy5WU1FWwQT9cUwo7bh4DMfcT+VrsQAJpFHsuKCDxpTpKqHmVFai28zZHZsAMPnQb4ORgbhfYOePQoFMEm61hoFvDdh2FuDk4cg8efgJeeS+Lw54+EJ3YsHiV0REdSC84hBJC7d+3g0PQOyhdPkO09SH1+DrOjNghIJt2iOKA9HTwefI7BSFjIOQEc78VNp9aRLs9DWZ7sajLW1JpLXYWpW1Hjbsn4qbU0kAwQfmeCpeH1zqiGtpMmFSQW2Hmid1PKckvBLhyP13yhkcTdmVDS5YCH3q8xuBpz9FZqKrqe5Lz1OtL2pNfjzx79Jn/8Z4/x0um51bdxBWUbkLblmpP00bqcbShV+/sQxt1u4IcRduSXkNiQqtyQHkiPqLxTJp0mvapq2DxAWpAt+xqUNShzKG+BTge+/ATYHMoM2hVMjMCeCTh3Gp5/Fh5/HGaOczFXXYtIAlkK538CcR+fBd44vYOD+3ZRnTxL9tJp6ouLMDodCzAOuKQgVvnrBSzMyX0RjQSdWbtA1HBOrKQsk/YGWoon3ewVAafV3IsMLUvrbAzLcN6RWkAq4b3XO0fBydGvhHBeIar0EHT/WjkhHJfGr/SYfJgKmcDMHNieS8BTAWqYbDG0b0+YMLSg6kLZCdUZSuh2+fMvHeV/ffg/rDIeV162AWlbrhlRh8TFc3kuLh5R9aeRahe3Iq66LyLK+HGEiddFKsU1iB12NGumTrSUyrAtw2V0iV1VNEI1S195Hf8WnDwDpgc+9Dve+TfA7YV/8SloPQeLR0MlcHU4rj1qmpZwjljsdxeiCo8DM8dOcnyxxSv+u/+W+mtfizn8Cqit5nJSKyCmgud0JbmyOwLWDAbkUrGWfvWDKszyc7jybbGHy+9q5EyXrwXkaWaaWkAu/s57+n2RvAdXCfBSQeagGG5ToUWr9FhCpXZfBosrvfudgJy30jnYhVhcvhRiWvXkGNLEBz0njYKmzmX97sKLXu6cpG1A2paXVTT7Y70WkVZKGCIZ911uSnpYRGIkM4iF9G0k7vQcYi04JL6UOjn00U71qu5v8x9XdQYmBIHeGYT0oPXrWtC5GewirJyE3kvQfRGB3IvXtwvdb3DhfYaA1HJ43+mWLLW7mPEJ8vFJaIT+TOdJmlvkgufKxEX6tffEjP9k9q/MwiwjFva8EoDkGT7WwaoQKcd1NSsiXabrBVBQ1qCeI8j2FJB8BeTgdF9KENFKD2E7Pq3VUiVEibB9F9ydVS+8V9ceEn/TY/DheH1ww+HFzWv0nNW1WA4eu0OOsf/wmaHXyyPbgLQtL5sYYj1Ajc9cqjQQd1w699VagCrt8DqNeJ+eQuoBav7XYcStd5yYLDuF5ClNENWJzm1fxZUIvy+GI5ogQmon7OlcPLuZE8SyruuDcGXWacVzZQ/68HkZOOsc7tQpuOnwRbbmiKnaFmwd6WpbnJ9SlOcBsDI5J+ekeKcqWGuh8JvFEhkSvaOGXWyOWCdF+ZfDDtg8WU/vsFBw1SNuSAUQZau5nhABbCWdZf1Q1fTcQhGOyVtxlZkirJtMKvpxN2R8uq0wXh7GgjVUVHG9qhOB3lr5fmpPuAbaoMWCX4ljDuE8Qv0+R7hGgfr+MpZF2gakbXnZJJm3rVtKJP6xE5ntH0cAaTVxSOPCU8Q25U1iYrMjtpBQIOoSSwIpoRqu1NzRE4sVWSINIR0Zbbm4/tFSR1OJgE8b+BbiJGwQUhecw80uwNJqRVxTE2hIsiTJsm8NhN84F39Sr8uyopDW2UURKk1fvMfuxiRYA5QCEi640nDgusENZqFuYzyrL6PBQkytq2S76Th4L2DRBxIXrKWhFplFAMMqtEnvdYXgkev5h+34MGbOyPiVXTl+76CsyzFnPhJFvJH3ZQB6gF47npOzAbhaYo3mhn6rdlsGFypgM06enuVXf/Pf8udfPXr5w79B2QakbXlZZaP0+ApRqDuIs/z2BdY/RuQ9jSJWUBleBaID0kwe7XekMSQ9ztXCI5sjabO41dxwGy8epYBUIRGnGaSVSQ8hOlQYuhj8Sjsk3cIg+FwAkEzIXckzUY59RkoCSMZAvRbCMgkgGaVMb6YMERl8BbYrFkke4kBVACTng8mbApKBrCHnMsC+0+McmhB4osXXd615YU0OrGelVXivJwSPbjeAs1osPgCP/iCXYyy7AiDeQjUi284IgORlDG0lrj21fspOdJPaAEjdjlQ0bxYyHn1Q8lAZcBlnZ5b51X/xn+h0hxsXXj3ZBqRt2dJyHLF8LhZJmQyvVxHbnj+HOMy0UZ96jrQmoRbTnQ6/0dJBm5lhcjVkGYEzLRu0GF4ryDm+dWInr9qzn8bf/AG4+67wKwWg4AbCEN1cCt/FIClBY0YOWScP2/E2ltjT/kImhfvNlJQ9p8o+uMhsN753GkexMV6m7btHG4G6nrAXrY1AKyfLeQ5cTwIqQxMIn0u9uG4AjqqKYKG/raoIar2OAEZVRVZiPVhLnWUorYDJyKgcZ68dXXbd5iAgqdXV7UJrSSxFQ3SpFsHKXTPv7OrJNiBty5YWtXIuJEpIGEFiRFpYVd1xaSg3tSYIy0IpVEq2Fhjp2GgnYQXawO/qVxTpOou1VWR0AdHU0b9JkFxtTRP++pS8ECow6CYUrfSneS2xjDYjgB7iaX6YwJCw4XxwhdlKLIOqF09JyRcQLD0HvgSnNG/dRXKsur7GdqyAi3cBaDznl93xwcqyFm8rfFWFXZv4fcjZ8tb2LR5vK0wueV7iCrQSq6oCIOXhOKtSjgUj9G3drrIbtW5dVQXynwlzAb2WeQSxl1HWfQQzMzPcdtttPP/88wB88pOf7PfNSF+f/OQnAfjQhz40sPyOO+64Oge6LduCPHtNIlFhlGghwflECrUkNB9pBMlh2sXWq7q+jFDbTyLEjvmwrEWsZj4DPLcyz7fPHqN88nF48fnwa7UANMbRIo6WkuK1Ml7oJ6RtDhVwTD28GhJDajShGEHapG9OESaRALV+GfxSeJVRAWtspdOBVhvabei0xKqouvFlewIEZVvIAlX4bHvi+jM2sPQzeZWluMJa7dBptSfvO+2QZJq8yjKSHiqL7fVwA98HUkRZyXaqCm8rrLWh2raX4+22od2SfXRbsLwAK4vQawWgQs5v+FVVAbAY4noomeHaqJixLgvp3LlzvPOd7+yDEcD73vc+3v3ud/c/Ly8v8/rXv563vvWtAHz1q1/lD//wD3nzm98MsOEOhDcjE5WzrB283pYbV7Qz7K1IbGkBuVfU6TSH3DcKMqMMNl0cFnX6lAivrRa2W4R9XUvlQYelg1DdV5BzXkLOo43w9uaJGTItBLQmrOXsc89hDx9mArfKuaXkcTUtwjRRLaUBkjzJe08c6c2yjIJ4iG7BEGtBA/4uKOq29P5pL4bYiRN3VV7Ib/rWQsgdKrWoanIe/TI6iT2dEd1pAxXYPV6LyXpPWVWYrCQre1TdnlhSwYrymcUl5A8XLC1nwXuDJ6dmHZkvpcacB3rBGnMejAvGUIh8mkrywfoWUmAGlmVIc8tjlYxwiM45fuU3f5dHv/YEpZZ5eplkXYD03ve+l/e+97188Ytf7C+r1+vU69GR8U/+yT/h7/ydv8ORI0eoqoonnniCt771rYyPX1464V5iZ85LcdNsy40jSs2eQlpOvIg8ezNEoq8qZxAwmgjvtYbzcAZG6rprI5aFkrMrNnd+vxmShtBLBGS035a+5pExWSR60EpgxRgWjGFpcZGR1jIT5zH5NNFSle4QIF1SmZzNlGHCQAAkG5JJfaBA2wq6KxGQem1xe+HFasuKEMcJp6fuRo0X5XniztMxSQEpvPJMQCA9PG/DoXls2RPwqTLKbg/nPUWe4zJDbjMBoWAFOedw1mGdFy+fMeQaV8oqWVYlgBuIdnJkXo7X1RK3Ysg1sjYQUPJ4XgF8vIVPfeYLfOHRr23iNdqYrAuQHn74YY4cOcLP/MzPrPp9p9PhV3/1V3n00UcBePzxx/Hec++993LixAkeeOABHn74YW6++eY199Htdul2I2VycXEREEB6LXA3MvP9IwZ5Sdty44pHlC1IZ9h5BHzqxPz6CcR1t4NYgUGdFKPEDsDp3N4SlXkHUeTq7tvDtRVPUttF7Y80k2aJSGgokPP8M2Rs9gCv2THFq/fv59af+SlG77gNwwqxJjhEeE6C/H0b8uWpCh2PR8sYVcKiKyvoBjqzU4ZdkiPkNBcoxJTyPFg/udCpfQ71KrLesmBR5I2hGIuR35KJ0q8j67VW8NZhXbSQ8FBWFVVVYasKj8fmhbjjag6PxzlP2evhQ66TkhPzPMdlBZXJsB3bD5kVyhmxlRxXrnUCA3lCJavFQra5EWZfXovn4nwA02tjerWu0MyRI0cu+P2/+lf/ivvvv59bb70VgKNHj3LPPffwO7/zOzz55JPUajU++MEPXnAbDz30EDt27Oi/Dh+WRL0zSC3jDoMl9Lfl+pIMKc0zRnSNXYo45N44gwCHppambSRSIDLJ90pa0LrO6X2lfLI8+d2lp6ReOfFEl6ISF9RVqa1PVFWXQ68q+eshxC5KikaNvJbTTyr1VhS4LWMMpLLQkziIWCYpkeBqjExqu8aaelBKjMeEPJ20uoLX3KBAQnCBkq5WVGUTNpoP64bzdqXEo/qNBpPTVELEQA6W0OBNlglhwRicF8vHWisgZB3ee5x3WCcuO+9cYh2F987jncd5j/UeZz22clSVk88mWK5aTigLbkYlmCiYZll0LWYmWZ7H99eIbCrL7hOf+AQf+9jH+p8ffPBBHnzwwf7nX//1X+fIkSMsLi4yOTm56jY++tGP8pGPfKT/eXFxkcOHD/MlpCDma4jZ9CAP37ZcP1IgHXc19nOcmBJ6MWkBzxDjI2PEG1y7w9aJc0EFoxEGwWo4PqQMvfFk3WthPql5+CCkhVPExN4W8mwsE608JWUoWPuwbH55kZnM406egp0TcHCaPixXHagc9Gx09XQ60KjBmFK+9WqlTeGvlGhdOSXmKyAFkMqQC+StHDfhve0FC0lp1urmC7RvG84tLySPR69wPUxHbJi2KDW6D0rBUur1JCk2AJFYLxYXXgJGQlLAGMl9c14+Q3DZEcDLhk1nYQLkscGV6K0Am6/XcFlBI29gigJTrzNgwSrwpMeocTNtkugB44IH9lq4ozfx7nnmmWd45plneNvb3rbmOlNTUzjnOHny5JqA1Gg0aGhPjlXkGDBv4J4mzFs4FqxTDVxvy8sjGkW43HlyRSz/ozGO9YgnNuJLmwAoqIyG9Swx/0jBqsn5URCLKPUccQWqtTRB5J+9XNJD3HHPEwsQ3R6+O0OsTKFFdFJraZxIbe8Q6/qdV5XI5lB5yXupjFgQ3UAx9kAtF3dRoXTuK3W2idVzXoxLiRNEV1wZXHVlR9x4jhA3Ups30NyBfi5VVQEGXBI7yuuSUOq0/7OVxFkzZEtrU7t6HdTiqQSAemWJ9548z6nX64HqbS7ekdULYBljJE8WKy49UgvPYtTCydLWHoHKreWc6kW4TmGdMFyf/tO/4Ld/7z/w1DPPretqXCnZNED6vd/7Pd7xjndQq8XH9CMf+Qj3338/P/7jPw7Al7/8ZbIs67vhNiLzyIN1X0j8nod+yt4814Y75UaVJHNjw+IQ5Xo5oqRkBSOte9wktiDvJcvSvKSUS0WybhuxOPQ3mnZ5tcL4q4kNx/UiAipqO4CM4QxiOaWRH3XVqS3jgLyoYRpNmWUXSXdUrUPnEFBSl1YvxFgMEnMp1AXk6PcKAlYfzfWKT/6uVspHr3ICSE5djVWwipzEeUxQxhTg2uKKM7oNE+IpwYLq5weFc7ZlyCVSN1k2eFoKBAkRwuMFmAKoREDKJO9Ljz9YR+n/Jmzcey/Wk/e4/hhAv2SR5llB310o11CTXcPbWhGPrx+kgqNPP8tv/9s/WPdVuVKyaYD0R3/0R3zgAx8YWHbvvffy8z//8+zfv5+qqvjQhz7ET/zETzA6OrrGVi5N2h7+0xJMeWm8NtKAXgYn2tuA9HLJRqyZKyU6j54i5iBNhb87wjo2fB5BKjFoZXCNTaZuOY1Paai/QGjVuj1tj361RT3/i9AvbqMs1DMIxXuOJFZEtJY8ct6vBr7/Va/jVa96DSNv+h7Ys1Nm1mnRJJtBpwjVAUKlg5oRN1gZAKmqoOGhGSpaA4NRvMsRtYpSaIUYCUw/6+DkkHshI3joJ6b2GYOB3qGurHoDqhWwoeKBurzKMHp5DlkVthmU+7AjxxCsKYkd5XmBqTkajQYmM+RZTr3RIDOGyhZkmSHLMqy1VGVFp9sJpyfgZTL563uWqnMJjussXIs+IIUpovGJyy6LybIKVteQbAogtdttHn30UR5++OGB5e9///s5evQo73rXu5iYmOA973kPH//4xy97fx7oeHGlNIHSCd1e50nboHTjicZE0jz9lMyg4HGQmFs0QazeoFGDESKBQS2OtDp2j0gB13m6AtI4V5doo3EvrV3tEaDsIa66WWLOEcSafGlRoByoV4ZmaTC1WuiDFDSVR5oD9oCuVA/wrsS7StxIJbIlKzRwQwFZiO0YxCLJfBL036iltNpTHSyXtZ52JRwYE1xXelVzcW35XKwfjankobxuFo7TKGutinEmrWigMRgbONc2sVLS88xzjCvI8pwsyyjynKwoxFoyBpMZTJaTGUPuoShqeO9CYYVAivAhvmSE1NC3yIOBavrkhIS4kA6CSV/p8WkViw1ekiskGwIk7wfPYmRkZICqncpDDz3EQw89tJHdXFSWw2u0jGCvD9q23DhiGAQMZZgpkNQQMNoH3IOAybNh2SiSUlCGz+Phd12EGLAc1lFHVgtxkzUQEFsmkiNu5/wJ55Vw5enTN0asvLAUjmsmHNdz4f08kjA8zB5MOWrV/Ar21DxYj/c5mKZ86y2mk0HLw4pCcQ9LRYbM8CmDsi5tKP3mMdhoLdTGpLDqeV1T1zMyw5aRLgvEhIuJyQIZIQ+A0og14/qB/lH5y4iMVpbLb8pZqJYDCaAm39fqQqcu3aCbb/jsiiKkM+XkeS7hjHoNYzJyW/WZbnklrLyRqhkYdpHoYCsrxIMMXOkw3ouuyzJyk8ux1PR4V2HMZdnqsyRPZBZeQ3Jd1LLrIgrhENF3vi3XtuiceaMkiNSppC+DAIo6ZZQVN4J0i70deBNyfxgEVGpEZppaTBpzmkEsDgWjVFpEF6Uef1BXA+mimtC9mTKHuOT+HMm7+iNirEzLHi0Rx6UkkjFSJqHy03rdHuVKG9+VYp6uKMlMB2MqfL3C1FqQLwOdQAU3OGfxPR9n8i4kmFqLzw3GZAJIpoC6usqUXrJeaymlj2j+UYjiGSJ7rmcFIEsbqdzqbhuIjRGVeBEAqT4CtgAX6NNa+sh7yJrhIgcLyyLjcDF3TJZhagX18THMQJVzhJWnFkxmMDajVlbkCSA56wSQ1ORPxAWGXqHuRWvl+PukBhNiRxkUgWWHXB/KwAi8BuW6ACS9RbXHzbZc26LKMY3RXAoopQSCnNh2XC1jXabb1zYTE4h1dCi86kgbc/2tety1MlsNia84JK9Hjy99aYFSm3w/h9x/SUiZ3cm5Xa61pC7JZYSJ+GXgaHg1wvEvJ8elMbJecp6OlOpuGM1yivFxmNoRFJ/DFRZDhaGEvIS8ByY4Bo3DuEKC7dbhQ5FV4zIwZT9e4bNMrId6iXG94PLKZXTXVcRzOE6kyzLZlwd8YNHZEOjXv0pC0FwcE4gaLgT+iwAQeSHsuDITVmGu9mQNcivgpAw8ssRFF7avXIcBA1DAxmDIGw1i2408XEiXrC/TqbwoMMqq87FSw2p3jveSoyQlhDJwFqM5VuqeMyGOkQXg6xeZdZS9HqfPzjC3cLkUos2V6wKQQB66b3PNuURvWBl+PlU0npNKh/MnEqrQlYtUJC8NlSelPfv9i9TtNgYcAPYjuWvfhVhJmlS9JxyjRSyhMQSE1NV3S9h+jsRi9BgVgJRIQNjnRFhPgVAts828Hy3igjsGPIk02XueaOksMzhpVysuHScd13uAu2ujvHPyEPs+8lPsePfbMRNjOFdhQ7TJU5L3M5o03daTFxYX/pXWAhmNfBpXlbhK+vyYzJA3CvpN8UZGJaBulOO3UVFbuEE0HZRnm2ZmIUq4zwDMY/JoXo8ursZoiMEUUBNlHSuRIyBVNRjoHtsn+tkQQ8oDcIWR7yef5vLekzQmzJN4U/hbBUsvl3hSZi3dXg/vnJQYci60bsr7LkIXrlCjF1qcey/WqNrnWk/IJvtJgO3F4yf5m+/5rzg3M3cZ12Lz5boBJNiOHV1LoipjhKg2VohWDAwG1tOwd6o4VZScoC0jNG7TGFo2goDDBAJMCk5KSdd7ZIxopY2F73oIuGhCbQp8GbHDrMao1Crz4fvdRKKBHr9a7xslO6gqOU2EhdMIaWGOQWBcLcoCoko9sZTScH5WMdrE7BjH1nJ8DoYeptfFVD1YXoFOB6/xmr4ytThvKcsS7w0UNZx3OO8wtRpZnoGpkRUZpqxJOR6MtN+WnWxQ9IfJHZNZOTYFlqIKllBKNoBYey5hnClwqCVljBwf0G/nMHB36kj7UCcuAI4LVknfXWYi80DdgkWoquC9WGRKKccENnqBrzR514cyQr6/O+88xguxITOGXPelFH3rAuADuVpFwX/gGQBM6z2z8wu02hfrJHZ15boCpG25diRHFPtBAkkLmbEPA5K2Clflr0oydes5YgO9RrJ9tUKaye+V4r0DCeardaA13RQYNF7UQNqgnwSeQoqz7hw6l3FitW9NMO2GYwuNucnCbyfCb1aI7DeI+U8blacRIAIpoXUCAaWFC/xGwVCBOB27HlB5h7UlPtRdK5s1TFaR0ybvtslabZiZDwrSxoKeSLVpa3v0ej2s85R5ifcOax1Fo0FWFHjfoJZnmFodejVMzQdFqRQU2DgyhamAySGv5G9ZlyJvxoLvhFBTsKA0STQPgFTkEl9J+wDlCjDBinFrTXG1xFAeLREX8pK0fE89DzXlnFhGeU1chASXXTNs30rRVKoM6qWMr7oFlHhgnQy/dWQIqaHIDEWWSzJulmPIA+svbMOFc1NwTUVdmNegbAPStlwRqZKXupRgkAWp79WagvNZakqlHiHGd/Q1jgBOg6iYR4hxI3Wlpb2PcgTAJhi0zEYRF58CiQ37uA2J0cwSYzOq3EcQ195+hLyQgk4TebjOEGM2G5ElBHheRMoofQsBpKfDMV2KKCiFqEisZOGc9OJxchXqQd35/hQhvNRIQHRdt9OlrErKqqTb7Uk9tqJDnmVkgVGW1XLq4yNkhQe7HBRvTaon5Jko6MtKglGbV2M7wW3mepJL5IWKLkVRE9Drm+BpbCo5QQUvH+7O1FTvu+oC+OQJicAh4KTJxXU9v0xAqn+ndWS7ZkTcmZmVGF1WQrcQcA1uOik3hNSwC+WHPB5LpIXnVYEpjBidfeZKLv2aakraCNfQ++jeu0ZlG5C25YqIgo2yvtTFpSokXUcdI8PhW7VAEjIuBbHUj7rjQgYJEFtLjCd/tRJDyjQbTs2uIxaVuuM0j6lBpEjrMaWKXSuI707OSc9LgeBy+ExtBJBmEHB7BokhHV/HNlTdpmA8jWESg9FKBt4JOcSH6+OVOCCWiAlfeu+xLryqWAy0osQXBbWcQFP2ZLUMYzz4HvjgqurXIXdsHJD0TkmsLGMg84HokNDETdYnGPSD+5lJtHfqAtSPSgrQ9YkuubgS/ZbtWQJseS2451arjugRYgiy3DhxqWUuHLtsox8H9MKmS4uvgsdhkgoOLnFNhvPXc1S3nRIvAhHipVNneOHYS3h37QHTNiBtyxUTzZFJYxxdzp/ZKz1ZH+uK+Chr3ChNilc33RjSyXUyvEYQFp266naG9/sR0NBadauJEhFUPAIESpQZDcfVJFKmK+CJZL8Qy/mcQqy2V3B57roFxJX4BGIlHePCbrrVZIpoOd6J0N9/PBtjymTkVZdMu5b6AofFVl0yb8mMifk7uZOguanICyuT7cKRWyvhi/40Q0RyaLpktRpZXojyNUheUj+6lgLCRkQdvAr5GvnT7Yc7L8/FbdaoE4uL1sCsXTOzX5DUhKz76iKVErJgouR5sP6UAzp8fimlR6c6ATAqiyt7uG6PWlHDeENnpYOtLK6S4qwOqd7Qn+SoS9JWcpwml/dZGAuXcFgDKH3o5/5nPvPZv2Clde1xkrcBaVuuqAxbB6nLLl0GgxZSWrXMEzvvpIQHdYVNIC6zHYiy1W1ovErdZ6vPxw3QxNDEMArM4JFAbxMBNbXulDZtk1+CWC5PElVgRiQSrEfUqjqLgPkMEjeaCcvOEltrrEd0DDOiu24Mz8Sh/dS+/y0Udx/BNXPKsouxFXm7Q9au+m0mvA+5Pd6HYgYZWVaQZ5Yst2QYMiqyrEaW1cnzOnkemHTGYHL193mpMWdyYsvzyxG1OkIEUWu5ZWboJkssiSylRadWWkLD7vcyclLDzpZJ/EyBOUyj+vsM77Un0XlECDgfnBK3oRFiiMlqZKak7HWw3RJXVVIx3Fm8c2QmI88MOUbaN4UEX0Owtvo3nVLAXYxzBddlq925JsEItgFpW66ybASQ1I2mQKQEBm2qN4kA0T6kNtsCotBnidbV2s6hLGxhJ2JLfQOpRuAYC9tvh+NYIRYx1eOtIcmzLyEgVCAWyU4EIC/msktBy4XtfwdxyX2TWIfuJGJ1zbN+oNPYl46bgJJj9PZbGP/o/wu3bxI7VtBeWaHW7jG20Ia2De0bAq24X3QUslxiRZmtkWeSi5ThyPMaed6kVmuSF0G1GBOsB0LAviPxjX5kcDOkADz9zq1991cQ5yWG5azU5StUUYc4k4H+XekRC0MHuerJy+qVsJDVAzED+h1jlbGnVc8viVepGXTB9VdrkhUVPuthOyVVp4vtlbjQMwlrMTnU8pzcGDLEnWecIx++y7yP10zbrytoXsOyDUjb8rJLmhircY60d5GCmFZVmCCy6TTv/zbEfQcSN6ojlosSKla/0UfBTEDjfjh4BF7xaph5EhZfIH/6UzKbJ8ZgtApDziB7bhkBrVsZ7JdUIRaN5Xzm3mrSRQqiauuIYwjQPYuAq9LO1yvq2FL3psbZRgKpwXW72HqGrbx4qJCwhjKIlXBWUmG9pdfuUpUVZVnijCHLM8Ynpqg36zRGGmR5jskzTKMu9fHymhQvzbLAOR8m9V+utIRV15uXdhO2RyyJEyw766HthBJeFMFkVDBSp3Ei3tFv0mfVdk1G3xBzmWo1pCXFcM34i0mwkPoWYzrdMn37yeAwgR6U+QJbWbLcY3xOrV4jU+JCoSQGJH5U5IOEji0g24C0LdeEKOEhdXQoGA1bUOlLiQejCBCk5APNUVr7kRwFMwXNAzB5E+y/BdwKRouHhn2rmlFVoWQFiIm+dSTZtkF0Ma4HPCpibTzNN1pE3HTHWL2amwJfGt1IHFB9EsgOBIwOAvsx7CWjgRT0JHQwddaGTgYGbwKDzceNiU53VL6iLKsASDYowIw8r1EUNYpaXQp+5hnUAiAVobGd5s30LYjNUpbh7vFhaqMWQT924iJzxoqLK1pIAbC0R1D/5eOL1OJK7j511eVZcOOlrrpLleQu73d6NQPfGnygOkgumHMVYhN5cBne57JHT6gz6sNvzcDDMzu/yEunz7G83FrH8V1d2QakbblmRBl46qKDQWZdiShptVAM4mQ7QOyHpfGVDLiDiwHSLZDdAvtuhh3T4SCCeyrR/nvC/heJIAgR9F4lW+rHjs4QLalLqWXnkDjRDOKqm0Xcjuqiq9b43W1hH0eJhzse/i4iILQXeCNwM/Bm4BWmwU1ZnUKTKSuL61XYrhW6twbmjVZmSI7Thc6nlcVaRxXwyDvoduNJNybGMI06TNYDGKVBfp0mbKbqGQVTh6byOjswOyP14qrgzsNCoyHHYgs56KwbzMFcKjaof9hWkRGYrXKcIfenXwUCiAkM6zmvcFxVR6yxRg5lIcc8YNlEbqp3Fttt4bMMm2VQtSiqEWpZM1DeM9lm3QlQghxXkfP7f/An/Nf/3cfpdntcq7INSNty1SRDVJHStDUuo6JWkiad1pK/U8l6mpy6gri4tMRPyr7TNhJ9R8yeu2B8n7hVduyAQ4dg3+3Q3AnnxsU1s3gCXFeKUSb6QKnfus20p1B6Xvp+aH7erwJRG/q9lvtpIQy6OQSEXkAsI3XgDI/hTXW4swn33Rr05hNQ9zIO44hr75tIKaSdyXgUQOEdtSKDd/0o/vWvhl2T+GYTZwoaJRQ9R9aqMJXagTqCHpwRT5YTSyobikf4xFDJnJGSOkYTRokU5POC/ZcrSk8PV8jn0GgJWPTSY3TgQtM+b4TKTSYn1C1jDbx+KR8jllSOAB7IukVNcqq0rYZD4lZ6V2urivUcu6kE1cse9HoSN1ISRRj0DB+Y6CXGiN1UBazKe0uSQ+bEWssIcbw871tclbW0r7HKDMOyDUjbctVEVcYU0QpKaeEQqeFF8psGYqWk9eRKRJmfCp+XEdfUYWLeURQD+18DB+6F+jjceht8z/1w7w4YzeDfPQ/PPgNHvy0KJx/4Jc1wXApI6rbT+bged/o3DTErFVzVVDe8tEjqLJLwuoiA0guIhTQMSAaoG3jFCLxjJ/zAfVB6OH4UdlSSCzVKjEPtRqwkTSrWo/GFgb/3Y/DKI7B3Ghcq74xUFXnXky1XGKyUC0oVa9CPQkIz5LkZwBbjzWDyVZk4YGtmczFoYFR0wwq7NWisCHlCeQMg1HYbmH5OrUGDdsGVQqVOCqlmBup58MIZySsKLs5+QVYdExveZGaIuXcxCQeXFQKU3Z6AUreLq6QaBiYck3UB0x1ZHi3XygHWU+vmWJfjAmGkwJLXR+j3d7r2Uo5WlW1A2pYNS1olYTUm2QTiTnsJAZ5diJK8iVjb7iyDjiFDzD2qEdxMwLuRWf8TyGO8ggT7l4mOEk14nQr7ygHqu2DibmjsgZEmvPudcNsU3DMBo7k8qG8+DGOLcHYEvvL7cOYZsZgSyYiWmOYh6fEuIqV8LGJJtRAQaxAtqYrIkNM24p3kNYMAyUkEfKeBPyBakLuB/aPw42+G190N970WdhRjVLOGj7xpmZFTMH4GbAuOOdmWllpSS0wqUAQNqqwwkNm+yciLnLyewZiFsgwlaywWT5eKrvNU1lCr16UIaCiRkxU5o2NjmCwjy0w//hZHLg3YXylRCkCwV7MpOeM8vbtC+dmGUmEIPY2Cm9aK8rfWYixkPh8cp7yA5hj9ZFvyJO9V7d8NWH/aQLDIpcLDaIN6q05mxLYuvSQ99Ho9YZd0pSSRMdAcaVL5isVqsZ/4W6vqmLqBfELceD5cA3ttUr1T2QakDchw0P1CtN4bWeoIOMwx2DBPA/+BdHuepC4vJSyMIfGQWxCLIm3/4ImuuonwvmYy2HUzjOyHybtg50GY3gl37IPDY7G0QgXsb8LJUZgah94cLJ/qH0s6sdTjTkkWHWL783RdVcNqMKRApMetaZHaKK+NlAqaCttWENGxHM1gZxNGR8CPweyyIbOG2w8WNEpHY9nR7UDPCYFBxyZUVKMNdDJDr2aoKTs5C7EkL4rX5x5T1MTd5jI0oVVqiBq8MRRZQZZnUtyzlpPVCop6vd+91BQ1+gVMteBpP+B/JWnHqblWQ9ovJIWqirqMRJYAo0muaP+hdrLE6p1oQg6TCeeiKwcL0MCGAVe3pRUfihxqBXmtwLuC3OZYm+FshvMe7xze2tC111OrFVhvscoeJ6OoZ+BLvOuCgV5pOfr8KV44fnL9x3eVZRuQ1ika01B2l8YybkRphddaoq62BWTcSmSs5hEF2+N8T4K67NTqeQZR0svAa4HvBX4XSRhtEfN93ohYXq8gsIvq4/C3/weY3Ctb270P9k7DvY3BZPkcQbnFSbjzdhg7PzdGVdoU0d1mETDS5gwtosU4lbxPXZLq3VEwVRBqhc8KbE8RwQsiQK904U+fhCdPwV89Dm27zN6RnH/0uh0UeRu6beySFNa+E7G2ZsLx1BFX4I6RgsZUnZsmHcW4h3qBcQZvoZfnFIWh1sikMoGtoKgwzpDbgrwQhZsX0pI7z3Pyek5eLygao9AcgdHRoJszsURMkVgPm033HhadAoTCT8YECylcgXqoGFHZwZ8YZP08TB+qUE7JhnRs76XGXB/IFFhT9Xk5/sjgCM7rUJNjqI00yDMPvov3Fd5bOisOW5VY28WVJS4QTHwONrdQz8hrBSMjI0KUaJ2FPOf0qVl++O/+95ybW7yMY7w6sg1IF5F0XqfxDOULdRBwGmNw5qyzXy3zny67kUSV6xyR/aaKNwvvU+tyMixvERi64be1sO4xBNyUiaaUbLWgtDSQYQdk++GWu+DmfVLXZ3QMRptQzwZ1R6+Cb56Bl5ZirsyQ6JKcSLRQMJ0iWkENorvREBNdNaqgOVFa+Xw2nNMZJBl2nlhaKf2dVojoWDg+Cydb8M1ZuGs/NI3ji99pM3a6ZGRFVmwRK6E3wthMNGoc3D/FgTe/gak33EN2cC+22aBjConhV5A7R2ZL6IXipFli0XjIggWVZwJIWZZTaFWGoiYU71otuLkyMA1ijk1K97gSwaR020pyQAgqffZJoLNr/Md7CcJ5L+lAJpck31pNACnLAkuwCG4vEJp4egqJpbQR8TBQithbcELttq6kqnpUVUlVSSqCMRnOQa+y2KoUvZN5rLFQZWT1nHar3S/GmuU5nZUVer0Se412iU1lG5AuIqqEFIyUsZQh8Y8MUYQ6D3NEynJaIaDLjQdIy4iFk8oKEUxU9JGeQm5IHSdLtKYcwkRrIYDUIboAm8RkWpFJyA/AwdvgVQfgPtbWFz0LXz8F7dBSOjmw4fwnBYeETMsOojWj94pORFJAyomFZpXQcAqJPZ1E4mEKcjNhnHyyTQ90Hbw0LxbjPLBnEiYKz198p83OBZhegVEb43D6GgWmmzXuvHkP+370rUy/4wdhbJJu1qDlCzLryEtHYR1ZVUJ3Rc59AJC89DjyuVRqyHLyPKPI6mR5AKJaTVqDa/kcU0/A6GpJcH9pRe5hWoj3A8RB6fFkw8wzx2RGXI74kPTaFKp4NzhPvQ/EhSEAvBzxFnwKSA7vKpxNAMlWgWFnsNZTVhVl2cM6hzcei5UiElVOq92ispIrluc57VYr5Cdd+7INSKtIOijTxNyOBjKL1wZuyrpS110XUZYaANeKAU1kZj9DfDzU5XORko3XpQyD0S3AK5FKBw74AmJdLSEAdDosU8BvQ7/gj4KEZreIqBq+hFmr83BuCb72OfjCv4RzLw0cp7rZUop36rDRPqI6aQ6ZJX01pYxABS3LoJtPE1hHw+eXGEyqHUNKIrXCvhaS4/mTp6SlT+HF21RYyCqJtf0toku1Dkw0ciYOjFLPS1icheUWeW2C0Yk6puulQlC3onI9qobFulJq2OExpcNYR16vUzQMRV6QNWrkIw1M5UNyaAOK8Op3Ln051UuaeJv6OPwgltSDfZsrRT2DCSV7BFAlhyJMQweSejcJaE09KJJGaJUO+UgdckfDjuKMESbp/Czddovl5WVanRV6ZZc8LyDz4rJrQ5ZnFLWCWlGQ5wV5njM7s4T31751BNuANCA6l0sfpylkFgzhwSYCkt7mWiGgg8z+dYY6SbSqcuIsGqIVsFoc5UaRVF2oK9QTW4kvEa1NBXOlXTfDevqSkj05otpD3YbcXFxnOAfzS3D2NJwYtOcUXBRwFHRSJ00aFfHJcnVHqjMmBaUW0XLSWJla08MpiwXRJazfa5Rksb06oSaNQ2mM03tHx7Zpz50le+kYjeYUZrSiVpuGrpGuCN0SfIXNwHrpWOrxsX5oLrlHrmYw9QzfyCVEZArpA5TX6FeZvqLuudVkmHg/fJXCSBs/+JM8JM5m6uYzEstR66of/0q3uYnkDINsywfyRx6a7tVqZEizw8JaiqrC4aicpVdJY8Rurye174yjCnWesiyj3WpRFjXyPOP4uWWePzlPtQXcdbANSCSYL6oAAErISURBVAOiLQgUUHYgs3YlZNWIyZc6O07jRkrhVatJc0DGEIU6Sby1u0j8QAPaNxoo6Y1XIcBzLHweRaoKlEhsRZNI55FxHEeqM+wC7gHuAo4g9PIG42HJlKw5Ygb7VqwmZQXffgFOnjvvK80zUvUaQuUDllHKrdIJRx253gvJdnrItdb+Rgo8yjqcQoBkWHQSNJVsq4WM2Ythe8NyDviXQ8te123xhhPPsOvLGVNnXuSW2++htucwdSYlJ8caWOngiwLG5Q4XQJagOaM5RnxCFA3I65Z6w1Kvj5MXTRiZYPMrMKxXUoq3glDqsluFUJGn0V919aVZcCm4XikJ019TlzgmFeQeUzakPFSeU+Q5M7UaZWZoVZZWt6Tb7kJRYZ2Vzr1YTG6o1WvUihp5nvMrv/0F/vIbx3DbLrutI6NIQmUbUTQ6x1Z3XdocTmfy6rLTOZN2ZVEwmiQ2gZtB2GCasKmMM8I+z3HjxJeGQ8A1RDmfRsB8DOEgKGEhjScNu83UfTdPTKbNMfDAm+GN3w2vHhf0GpbTFr5Vws016Dg4Mw+L53Ml1d2mas4k7/V4cqIFpXNQrTah8bI0vmgQBtwp4EtI/GgeAa/V2Johq4a9yL04GZZ74FNIDGq13Pth9XO843n4hZLRxZcY+9Yy902d4o5X3MUD/8XNGBco3mVFNjJKY7RJ5QzWZXhncUaq7cjs3WBrBVmupAEfmGFlorevdtxIRZ2lwwCyBqAYz/lMufT3Gatvb7PFgQm2uPEBA4N9Hjq8GoMUry0b1Jo1TDfHl4Zer0dV9ej0ulQhd26+2aQo5Bp1u90tA0awDUjkiBK8FfHfzxBZWzuI1ZsVhNI+kNqsLUdmsCnxYScxkN0J29lFnLB3EUV0GmGS3aiAVCBKfw6xEMaQiYBaI8HrP1ARXH/vEGthARnjOp4cC69/Dbzrh6UnxWodDmYsfKktyZ/ZCpybgZWVASWeUrTTufZwXKmfujL0nVpEpxm0nLTteQ+ZiChjUM8lFQWjJhJH2oWAtTLovhzGTN2DF3LKnOvBvz/pyE/O0GCGkzzH957u8L1vfgsZNYzP8MZgnKW+cxdYMM5QVR5fgMNg8hxfZGS1Aq9sxNzjczlrM+DQTF1nmy2rKVgd/dUYb36VZRAdpsNytcE0uXtMIF2YcMeHAq8eKOoFRb1GXssxRYbPBZB6ZZd2r03Z6+G9p77QoChysjynLMsL7vlakxsWkJRs8AbEJaKzUI33aK+dIlm/RgQjVTAKUjuI7qbpsC0QRTlNjEkZRPn+Z9bfaO1aljSWZi+yLsSW4sPz0wopo6PbaCbfJcTbfk7TOaJb7QDLvJ4n4Oys0NZev8bOZ87Clx6D3/kMzByFU38Nldgnat28SHTB7g3HkcaCUverisZ2OghILoX3NqyrFs40Uhj1gbDNZeBpBgkudSTv6s5wGoeIscsJ5H77L4l9k76DnPLFGgNaxKr8HLD01Ld54y/8jxw8uJvJ6Ulmdu1g8va7uXVyhzTkszI7z8abNCYmqY+MkNdq1MdG8b6H9R0hMxh11ekVUmi8koo9pY4oHGvEb/jKXAYt+6qITnWKEIdzQpcsMnCWTmuR5ZVZ5hYXWV5eZHFlkeWleVqLi8wtztLrdWm328KkM2CdFWp+ntPrbS3a1A0LSKoMFWgUhAwxVrSDmN/SJAbUh0Oa6cx9GrGWJsKyBqJMUm92E4l5aIKlJkSuEJlYW1E8kRK/xNrz2GHXm6oKVStpWR51cSoTTScMMFgLbxYYw+HpwEvHMN/6FswegaweCXceQYj5FXjxOXj+KMwdJdooci00WbUb9qH18yCCklrMqbtOr51aSNppNmXeKa1dKeN6X60G5Kpax4ku40nifarMwpJo6Suopxb3asnHy8CJTpc/P36Km3sdpufGOTM7wUHfYPLWp3HO4MgwjQYZ0xQ7d5BbqV9X+Bxn6vjcYEwzxFx0unU1XFzJmXgdcRfem4RP8XK4DTcqaf5UDlmGKXIoCuktFSjfJjOY3IARwonzHuscla3wTq5013RpVzDbgqX21vK93NCAlCEzWY+4Q7RD6E5iqRr1hqurLVUaynjyiNKqI4CUKs0RJMyeShsZ+NNILOFzyEz3KWK3063j9RVRy+IwAsiPEeNkqahy1vNLQ8ZVskxFle4kcl2mw/KMGPNbQsZ+PGyjePSzmGcW4M3/Pbxyl5gZepBngWfPwlf/CtxRIp1CZD5sT/OAQNy4qajKTSt86/Ho/aFJsDp/T6MbMwguajWHmD8VpQKeQypdeCLAvzL8dh6JHz0LPB62c4CYhJxOCNZy2jzt4H/uwOEX55lmnjPAdz0/y1h7hfrIGLXGKHsOHmDsltsZ3b1fOqfWPGSWbKROfWQyHH2a2LOahXKlJEwPvJVEXlsJCOWq2NXpuxVEp6zhlYfoZD2n1qwzOjrKyOgI1leM9EZorzTptDsUeZ0ylEeqqqqfEPvU6ZJPP3nt9j1aS25oQEqbrqXxoB2IcptI1k8BSZNcK6LrT2upabO2C80R64ji3oXErgzS0+bbDOavbEU5Q8yVWUvS6MIwB0pjKWp5qBWbWlJqicwzGJNqIrbOZPc5Rud68C9+F+6/Bz7wADx+Ap47CX/2O/BtnYZE6RFdbcvEhOfR8Fm/1/1pK3Mlt6hrUVl0dSTuc0/47Smi9fgs0YpSFp2SYnTcdBzaCKDsJbp+VdKCsjvD/r4PGB8bZfKVtzPfazHTWeFTL5xjvrf6TNkRE3Ez4OnZRX7ly0+GhnsFN42O8qb7ZvnxW26Foin14HoVTO3ANMeEWj9wt6dpw1dS9Gm1kcqdBdeXdeG4lGyRciFXk+EpAwyC65UGtVUigKYh51N35I0mtWaT0YlRfO7p2S6d5Ra9bodGt4nPPFXVxFmHtdLJt6q2lmWkcsMCUup9Vs9zE1Ea40Rw0dm85sioIlHXks7wlb6rIJfmcjP0vkDAaFdYb4XodtHZ9lYFpGHqsnrKhmW1HB5dVynVDrGM0pB0amFVyXYWEICaAxr2LCMry/Cnn8P4DvzwffCVY/DXT8Lv//+gMwnmTWgRTb2Wy0RXnU5SJom0fAUqPcaUfafXXb9T4sFdCHhlxPp1WuJSLSONwOQMXvc0EjMRjiVxUAGRSDMNHDLwPUWN/ZMT3HrPXZxszXFsaZY/OznPcq9akzizjNyDO4ETK22+9Z0TEI7pMFCMjfGeMycgb2LyBtVyhxxDbXpPuADJFOOKt8xOpzMmuOUUkJwkOnsHXr9PY01rbdLTfwL729M77UoTNEj2pxKcu8ZAXiOr1SjqNerNBtZbRjtjNJpN6o06jWYD7y1lp0En9DqytsK5rZF3NCw3LCCp8jlHVIzfj5AcdiIKYH9Yp0RmoZ5YkaFDBCaHzE511l4k28xZneiVit72+4nsu+tBtM6fAi7EOMouBPibxLwatUTSJNQFBgP1KfhPI9asKusa8OfA99Fjylcw+wj856fg2y/B2Udh6VnoLIJfAv8I0KFCio4uh2PQ2nipu/YAYqmcCceg1zaNfVnkvhkN57svHJ9aeXchLM6z4ZwWEPA8irje0jECuWe+D2m/8X3huCxSiknvwTPIvTIKvGKk4K1jDV77xu9h6o47mHrnj3C4KnnN8jJf+tYv8PXWcb5wAR3lw/GkqrFEyB1f/Ppf88//h6cBgykKRnbv4663vZPv/nv/EHaMS8kg6tFku6KS8i1XOSF12WVpGjUM2p49YlQulLr1XWKZo3Sqo1f5SlpJdWISQQBC34vlhPrTkOhVKIBaZtg1NUWn2SDznm63G6yjrTqdXScgfepTn+Jnf/ZnefHFF3njG9/IJz/5Se6++26eeOIJPvCBD/DMM8/wkz/5k/zjf/yP+z1RLvTdyyk6/0kv3QlEgap7ZRpJutxDDGDrba5zKv19GwGpZQbrmalL50IyhSjU663enT7SGvdZYXCGn+b5DLvx9PqkAXolDkwjYHYg/B0lWg+zaFkex4hrYZZPwQtfg/Zz+PIsFheOa6VPYFDQU3q2S45L6eVarkjJMMq41L5Hyr7U651aTCYcozILVSUuhGNVizAVBQYlbqgrUd19Piyr1Wvcf/MBXjs1yW07dzB91x2MHb6Z2t591MqKYqzFfW96I9OH9zPZWqHdWqbdbvG1E3N0qsG9roZXFXCy1eUvXxK2lskymktdyqef5taj3yCbnMDU6njTYGTvbsYP7QsnEAgHGWjH0niFL4f4kCQB+JSAD2LhmFXe6++S33jkveuK4ned0HgvD0AQ7kRTJOSIK5GXNOzuTKayxkNWw+R1TFGnVqvhqjqNep16vU6t3oBul8yEHlQGrIdj845zK9e5hfSd73yHD3zgA3ziE5/ggQce4EMf+hA/+ZM/yWc/+1ne+c538va3v53f/d3f5cMf/jCf/OQn+cAHPkC3213zu2tR/jq8UvkYQr/VuAbEmXKHSNyaI04QG8SCq3Vk5rzWLZwBdxBzkq7HVha3I1bMdxCX3jzR8lSgdwwWsVUrII3VEX5zELFA7iCO6yRi4Whe1xmkHUVWnYPFzwGRfKLuuPnwSovk6nz0LHFicZrIrEtfSmIZJcaQINY+1ERfPUYFLT2PxXA8q5WPcuEcXkBo3Y8hrr5vExmhU8CRHRP8v9/1N5m6+Qhjh26RvY2PY3btxLd7FI0R/u8/89NUK3MsnXiGky88xYvHnucn//VXeGnx0hIPni3l1T+y+Vle+vLXuKn+L2mMTmCKOrZocPP3voU79v6IrOadNISrZVJhHRgM3G9UqSsIhewrb6NlYxTy13LPKRhZsCXS7C602bAtGG1Asy715Hw41sKEi9a3SzZ43JcqOf36eVgockyjSzFaMVb2qOc5eVWxMjlJryqZO3tGiAyVxTtPr4LPf6ei1dtqtCiRSwako0eP8vGPf5wf//EfB+Cnf/qn+eEf/mEeeeQRFhYW+OVf/mVGR0f5+Mc/zj/6R/+ID3zgAxf8bqvIv0Uy6lPL5RZEAS4TrSBlVXWIVGENYp9hME6QEas8EP5OAN8NfIPzK2RvVZlECG6T0K9yoI9JavXonPc1iDV6OzK2cwgDcYXzW4CrBaOJy+pW/W4kF2wP5ztZXNimukiVFXcb0brV3DKNGbpkH/q3xqCFpGpwhci8VJBphGM7R3TZKaBMIOCl56ai8/AV4PlwDN9BwHOR2ITwCHBXs8bUXbfQ2H8As3sPjO3A15t4Y6LizQry5hhj+w5wqGYY3zHFj37uG3zbdnhqJXbCfZ5Lt9C/duIk//hzXyArahiT4U3GD83O8X/rdhkbG6FRz9kxkcOBfZgDe0OtuJCtZvQsNyA+gJC2G3dqhXkhMphhRRxiQBonclYSosuuvLrtsK0KXAndHGywoChg1EPDS505E+xjM1jKd3NEfQIgoKTLEAu0OU6t18Jg8N0O46OjlL0OvdYYxkPV7dLpdFjpOYzZijxdkUsGpHe84x0Dn5966inuuOMOHnvsMe6//35GR6VQzmtf+1qefPJJgAt+t5Z0u126WuodWFwcDpNfXXk8vFJ5AGHHtYhsPaUgq2snDXQvMljtQRVeKjVEER/j+pEaMotX11OaY6V/05jFXmRcX4+4o84AjxLjK/pS66lH7E1VEasg7CRaKSq6HwWzlBSwm5jEqgBTZzDnCKLiTufKeo1NcnzqWtTzbhNr2ymdXF1/+tvVnFqdMAYriHXUCutrwvahRo2bx0dpHthNsXsaPzUOU7shq0lbDYXULCerNWhMTtOoZYw0m7x+7wQjVY9OkbHDyHnMLrXoOI/z0WpdC6BOLCxyYmHw2ZwaG+O1O3YwPTXO2EidatcIo6ZkdHpU3GEmg8wKcGTDqmcti2lIsSogWSeAZJ1YMQC5C6ubZJNKSrDiivNWLKKyB90OdFt4Z4OLsYLKhAZ+aiHVMXkBhRMXoAncSrPalduI+ORvSmNJaFGhL1OW1ymKHjYvaDTqjDSaNJtNXFUx0mwy0mzSbHSviZDIRmVDpIZer8cv/dIv8bM/+7M8++yz3Hbbbf3vjDHkec7c3ByLi4trfjc9Pb3apnnooYf42Mc+tpHDumryV4jVNMz+ugtRHkpq8IjLpkG8dcfD5/RB1zjGLUiQfi1m2laTs0jbCI0ZqdcnDd9WRJLCcSKga8+gXWHdJSL4qHKuEBDbiVgLOxisrpGKAsWtq3yn4ODCNpS23w7HsIM4iWgTc87UmtIJiFq/Wq1Bc7MUQHUcziBxyqeRmNAMg/eDxtlOMpgGUCDuyv3AbQZ+6G/eyytefQf5LQdhYoe4nGqVqF+Tkdk6mTFQFeCDk3FqkmLvHt7+d/82f6Pq8g92jDE22sD1Kv75//RPOXdmifmWHN8c4i68VKvpka9/jT8/+iTTxjBmDPsyw/ve927e9773QF5I5YGRBuy7FXYfTEbsYtZS6tQMUwpLBCQIFhKRJdfXyUlhJduV38gPZEStg6pL1V3u93my5GJh5jl5kWNcFQA1bLTpoD7GoJ/jcmQV2jfQDwhUbeiuQGsF116m21ogzwxjY6N0V8YoAF9ZemVJp+fkmm9R2RAg/cIv/ALj4+P81E/9FL/wC79AozE43282m7RaLYqiWPO7tQDpox/9KB/5yEf6nxcXFzl8+PBGDvOKyXCLABA3W4vYoycH3oIE3tM5kA3fLRO94WeROMUTiGvnegAjiGVqUl6USkVUQzrX1FYJMwxamQroUwj4aB8qdY8qsKnqWW38dF9KNlHRdTWxVi0k/U5dsmoJ6zxWwUj3DRE8dB09Ni20qnlFwySOtdSHqiiLWHG7DHzfaI0Dhw5w6LZb2P+9r2Hs1oMwNg6NOhQGbyzeg/M5JvP4wmC8wXu5K6XlT53pI7fjcGTTYzSadVxZcd9b38DCzBLLbcdsaTnbKTn+9adZKu2q9/ywtMqSTlnSQq7ZAvDVb3yLg498Vmb5WYZpFBz5vu/j8BuaUAYroKhBYxxqI0RbVigmPjh7PQ6HwwdYz/FgPKbwUhnb+PDTMPq5CYMcyBVqGTknhWSdl8oG3uC9kRQA67HWUikhwhZQ64LJMM6ijf9MXacMKR80lXT5KsSK/pXXZTpVGaZ/63pxG8LF8PiqCm3M5ZwyA8+fafHUiwuUdutqkHUD0h//8R/ziU98gi9+8YvUajV27tzJE088MbDO0tIS9Xr9gt+tJY1G4zwQ2wry6NBngwTeR4jzH41hpHO9DkL9fR54hNXbCWxVSb3iw5LOuvUmVMV9EiEL7CQSEPYmr9RFpu6vZWIJntXmmmvd6PqoqztV5+wQk1ZTUWsqJRMrGKa/1ZwjLbJ6DmEALhGBVhNvU2BdS14B3FNk/Dd7Rtj9g29i59/7L+HgbrE4yPGZgQwcJc5bnMshA1+H3CO1OgOHMMtzdr7qdVAvYHwUcqnq/SP/0OM6LWyvh1vucer0PJ/9b1/keNm6JEDS662OvLPAf/zCozz1BXk61Ir8+/+fFW66ZVessD7agN23Ywp9Wqowas1AVHA4X9Gjhwvg1KAiyw15XeItHjAlAZCs0L41juYqiaX1epKnRCFuOqfRRLlqVcjfEagRAK9lso0ibwjjjjp+xGK0ZNFq8TA9hn4sKL24LixLuaRr3bVhvSyTHkl5jglFbW23S29lhbLdpup2wVq+8M0zfO7x45dwpa5dWRcgPfvsszz44IP8xm/8Bq961asAuO+++/in//Sf9td5/vnn6Xa77Ny584LfXe/igX8D/DFxkA3wY4jVpHTeFnGudamFSa83URLADKKwNehvkBwcECA/gLirlsKycQSgdhOLj06zsXCzKsuLOTvUWgrZK/0JhLItNY9sjthgsCKW+1lkkN6vZAmtfbeW4r8VuHtkhL1veQujd98N4+OirK3EG5w3VD7DluCtw7R6cXJdGFHQRV1m/dZCI7Qbr4+GHJwe+c13kltH4cD3KvbduszH/+EZFhcWWGovM3P6NKdmFnn4qy/RvcRZ+Eni9SIczti/fYQX/voJZktxLzaLjLf/1N/nu/7WD0I9gJLvhniNVOvLvKduHb3MS2yvlpOZTFyT5GRkZIXFeBMsmZ5YQ50KqhKqLnSDhWQ9vqxwZRUKyBr6UUNfYYNbLwN8z2J9D5MbTO7JilyArVD2oEYT1fYOT7EvA1tOwcoJUPXdaQpCqXWkoKTv00QTpehI2CN9ZdkQIG5huWRAarfbvOMd7+Dd734373rXu1heXgbgLW95CwsLC/zWb/0W73//+/nFX/xF3va2t5HnOW9961vX/O5GkKeGPmcIC0wVmgKSxhc0EH4tyNWMY6l7S+MtmiBrEMZcgViQu8NL7esJBIAmia48BbL1ysXC0zoWOp9WNl2bCGSai7aCKOF54oTDEduyV0RQSlWahrHTcVc34C5gf63G6OGbqO/eJR1axSQCfFBtHlsBpSfvVWIh5UAhs2tqBaKvc6g1JKE1b4JzGOcwk3viHq1lbLLN97/5tXQXF2gtznPqhTGePXGGf/ONU6z0LMaLPtdxWE2WwyuVx596ltZTz3KKGGe983vfwN333Uk+OiUMbkqK0Wmy+ihgMc6T29DgwoDLDBiDIcOT4Ywh63vpTHTTVZXU4Ct7+FDB3FQVrnTYyokxQ9iOD1c39A8yiHsM6/A4DE7IDTaAXC1ERU0YMx+ifb4SQNKxVFeiD5Zbn3Ma7gyvrrqE6uNjPQ5vQ6wshMayzJBlWf9lTJrAu7XlkgHp05/+NEePHuXo0aP85m/+Zn/5c889x8MPP8z73vc+fu7nfg5rLZ///Odl40Wx5nc3ojjgNxgsRalWgDK0rgVAUgXZST6n5Wo2W1L2XYYE1HeH/aaVrgnHcBcCAitEkoi6266kKIAkc15ayfGlakXnvGcREDobjletqBax3X36EA5f/wYCuLcg4JyBWAB5rn44qDSd14bZjQdbiXcqMEgyIM8rAaGigHw0FPAsJHE1C3XHfUCZHMzIGKP3vYWRXo8d7R77Fuc4fPw4/48vfZPucovMwrk5ONMTd/OlWvdat1HvJwPc8cgj1F/8Jrv27KFWr5HV6tz2I3+L3a9+jaxUGujk1HIpxEAOWZGT13KsIcSaoqu/Zwp85mkUFc47nINeAyg99U6XXuXpVkJG9N5SYCkyKLKCWi4ELHIpbFrUQ8353EM9uAC7BeQ2XHy92hn4jgBWr4Q8FHvtEzAsNMJ4p1ZQpbVIwjLvoNcNLsUuLC9Caxm8E2CmIM8KiqIgzwuKIqco8i3NrlO5ZEB697vfLf02VpFbb72Vp59+mq985Su8+c1vZs+ePQO/W+u7G1GGZ5IGKc+yVjD+aoo6LrSOn7K7lJjgEOW62cA0HMrtIAp7Gcm/URLIVPheYzV6TKuFjzdDlPat1kyXyK7TnLMO0WJLY4X6u2XEMkqTYHVdm/w2jT/p593AbaNNXjc1yavvvosDhw6Q3XkH7N0j7rf+zFiIC5kPAe8wo/ZedJvJ5YqZvCLLAuj0B91IbMTk4IKizER5Gg/URzFZHbKKHMPY7pI3vumNdJdX8L2KxRV4aanLo19/miXnLqnH13CFFIAnTszgbcXkxFmKvKBe5PzA7kO8qmcxLse4DFPljN56E7XpHWAKTJiGZGQBkPR8jCzzOWWRyzUxhtJKBQafeXoGeh6qcMEMJtgzniJYW8ZA1k++RRYUOSbPAqi4cLETEoOvBHhccmdWBDKFk0lE/0LrzgMIVRX9ShJlKcDnS3lflVRll7LbobWyTKu1QmulRafT5tiZBb745DFemhm2RbeebIhlt5ocOnSIQ4cOrfu7G108MmO8FiRDgEhdY9OIYj1GnM+lSvVKiCO2TziHgLUq511hnZRGrsrtUsjD65Ue0fXWJialTiTfrzDIhtOXgtECsSKE1j5UsE+p4prcq1IgeWk/ODXJB155hL0/9X5GXvkK8UnW6+JyA3ywyYzPyK3HW9sP2LugL01m0DbYZCHmYGycUWc1ATcfIlsGoRo7D1lw7WUSaxnZk/E33vW36bZW6K4IKeHYqVn+4xPPcqJ3aYC0mvzlizP85YszfYtxBKhlDUZePEFhG2QmI89zDr/z7TSmx8WiMzXwtQAc4Vy8QWyIDGdyWvU6trC4qpCeQaZHNw/pRuGiGW+w5GRYrPdkRVJPwlmxOAsZO3FzFiHRV3Ohhk7GeQElXa6AZO1AelEfkBphktDpRECygXRhe1AKyHWXF+m0V5ifn2FpaYnWSovFxUWe+M5JfvX3v7bBkb+2ZNMAaVu2vnhEaS6G983w+QwxOL9Wb53NlpNEl1yT2D+oiYCDlmcaQ4gOmqO0mZIjQLFCLO00Rqw72ELqH2rFhaXwOo4QNBaJVSaWOD/+FIrD9AFVowsHEAC+B7htepodd99NMTkpvirrgiWTBzPLh0q/JfRK8m4P7y2OiqyWY2o5OBsozWBsUHZaqc9WAkji04umanMEfDNSpa2FTo+sqDF+812M2hJnS6Ec7znNe773FhZWFlnpdsBazrUt/+7ZDm6dZr8SvlvAv330Cf7qyecofCaHZwx/r1dy76vuwvocbyR2dPBvPsD4YZ3wikXjqHB4cpuDzzB4Idzh6YyM4oMlU7OWzEFOTmZtSIoNOU25jYBjnHzXq4srLqsJQSStcOizQfa2cwGcCHGl4MIzGkMKg9MJwNbVqZaVvCrvZZ96zZK4ko6WtXbLVvZeTbYBaVv6osqgRwzQK/lClejVkpTsoW0+5hH2WoaAkx7zcN7QxUR1pJ5PnizXZZooq9ZOm2gNaeLrCgLW6l5UZt0xBIxaye/V3QeDbMo0J0lznQ4ZOGzglkaDPVOTNA8eIGs28SaLs2xvwgTbiN+pBLoW0w3VCGoWvATrweO9Dy68oCQzL3/VfQeiKPsDqFlXmVhHxkFeYPI6tcmdSOsOUdg7MLz6lbfQWVmg125hnOPEQocvvPQiVXAd4qDysHgJulPvw++cnePE2bk+UOfA679xlGanS1XFa9G843bykWboPCEWYn1qnKzIMZXHhPM0zmB8hslq4VQ9ua/I8NSCtSgOUHUAhrFxPuYvVV0Z+zyclJYT0oEbIMqFMbYBoYyarMHdpyEQ6yPoq9XkPV5LP9kK71ygrgwivFzXl9vZv3myDUjb0helLmsx/JeI4HS1RWMxs8QyPpoXXxJrwE0QlDixn9WlylzYlrYO6RKbmd+CgMoxhJCg1cHmicVYu8RCrlV4r7lUStTQfWjJopScocCmVmcNKQj7YB2+a7TOyN2vYvcbX0vjDa+FqR3iJupYoUPnIfPJiSLMnFTlsV2LNw4KJB/JhsB7Jiq9cE2Z2V/sydf4VK0QN1LhYHSHsPKKblSmwNjIJG97/weD0vQ08jpnj72In/l5Oq0uVVfIFse78Nuzlx4rXeb8YsP/5E8+z9hnv9CP65UY/seFZb7r9ttp2Ypep6Tb7vLmD/49dh25BVo9qnabbqdDvmucPMuY7jbISsgqR7vdAW8ZHc3J6pBlOYZKBhQbQNtA0ZD33ZUA9qGoVFYPZZACEqVmusmEll/ZWG8vsOVAQc6JW84Hdp7eGbaHr7rY1jwyoXDY0BH2epZtQNqW80RjIxkvfzsMtZCWEAWlBIsKOcZZIrdKmytOEi2o1UQJCvNhex4BBd2ebnsBAZ95YvGalLSgZYTUklwgujZ1Xf2bWkQQrSHNYLkZ2L9nB3fedRPftX8/N09Nkd90M6M334ybnsI16rg8x9caogCDVydznlxL6JQOVzocFmrgjcfh+9TvnFwsiKoniZYAVWDsuYQUEGJOGLWiXGB8GYmf1EF5bZQVWV7Q3LFHyu1kOUWtzlTR5Ht+9F1U3R62slDC8y/N8rXf/wKl931XZRthVa4lw+C1UFlWsCSqmz/55pM8feo0PeeoKostK/jMTRw4sJ+Zc7PMrywzt7zMG77/zYyPjbP04ilaZ2dpnZvlxNxZpnaM8cNveROt1hytlTkW504zPjnGnffeLflGeQ6NEZkQkEFeCyQQ6Jt/IBaQUXpNsIIM0cJcDYr7lHBk+94H118ce+cqrC3pdjt0Oh26nQ69bpeVdpc/+foxvvnC7AVGcGvJNiBty3mi1OSXS9JcHAWABWJTRc3Z6iKA4cLyEYSJdyurW0ppSuICQprQuI8SKWYQcHmOGCcKqpgxYnhAmYD60nqEjkEXYELmHfD+awxMAfQHgNcd3sMPvfN74LVvgr2HoDGGywpsXqeb51hjsI2iPzD1SgyX3Ib4RulwHYszDurgsVgcNgvuqXodZ0uszSiyAu98n97c7/mTqXMsuJfSGJJB1k3zCF0LfEExvlvo5LUaNOpMTO3m+x/8ByHuYaGCZx47yl//wV/Strbvxjzr4bT3l2w1tVZZ9qnHHh/4bIB6ZblpbJynX3iBl+bneGlpgQ9lsHfvPp7+6jc4+czznHzueb5+7iS3H7mZ++++g5dOPMPJl57lhaee5OAth7jjtXcJADdqMDqGIQ/BvzzSuY1HutKGu6RIqxuGqUlOHIe10q8lwSiZxQSXLAZre5Rlm3arTafTptVq0+l0WFpu8bt/9jRn5q+f+i7bgLQta8rFlITO8o8Q4yPq7nuMjREgCqT6gtKlU1Er5jHiY30b4q6bJeYDaVHUVPYQ2XFdBIxOEZrcEV1tM0SXYEmkdoOAncaTjhOtqBUieUFJVNqqYpnY2kKPT1tP3BW2ubPI+Rt3H+bA6++GN9wHY9My066P4LKcCkPpHZUDT05eeorSUayU5KWFXhvbalO12vToUBWekhxvvbCJczDGYrs5jSynnhXkVYXB4CuLKUuZmde1rCzBtRSArh/fSO6IzIgCHR2Tn9RqwkLLhERBrQY33xV/4zP2j+/jH/7Sz2MDADng299+Dvcb/4Kuj3leS8iEYKPigf/w1Ldo5DntToduVdFzjl/+7d+hVqvTXlqh1+nQ63So2ZKnjh3nvf/LL9Httun1OnRaLY48N4Pl/+DIHXdw4KabOHT3a6hNTMLEFGir83ou8Z9ur1+rT4YvWEgDCa9hHPoVyeNq8UOgkxsTqg5l5NQhc2SZod5o4fFY6zCZoVNB1m8eeH3INiBty3mijK+LVY7QKgLqIquHvw6pZK2xlPXuu8nqFloXUVYL4XOOWC3KhNNne5pY+UDVgc7ptYTPbPi7gDwECkjzDLaQUADS/XXC53PERFctnKqdX33ye3XnQSwTdAA41Ghw9/QkjcVlJvAcuO0wU7fcDHsOyBGZQmbnRtw4wkXQ/CEvwfrS8v9v71xj7Lqu+/7be5/HvfOe4cyQIjmkSFEiRYqSZZm2bNmtH3XqpnKbFmlSx3FQG7ZlGHkABgKkgJEgHwoYRYq4QAAZKVJYMWID/lDFcQyjceRYcN3KsRRJtkxZtqw3TYqk+JghZ+bcc/be/bD2Pvfc4QxFSbQ5HJ0/cDlzz9zHOesc7nXWWv/1X6qwsFzil0vccg+blljtKb2TSQ1aOA/agq48zgSRUudRPsRsMeXnQpouLqS+GeM1oFT/kSRCK887RPFR0S/yIvhaN2smdL3h+rfeTjMeSkcnuHnnP1B4X+sBHlta5vjxlwbW7XguLhVHzy1csO0nz12o8zYFLC0t89SPfjKwvXIVD33/xyhvSD1s3r4Tk2ao0XhIIbUZU5oxrvcNe8Va0QpmXH2hRl9SS0yEDcqD0Sg0ilTIDd6TJCnOWdIsxTormqAboBm2idYhtbgAXcSxnOHS5YxG6U+GBZl0ehyhb79aDlAzdQeSnnuJfu+OoR/tNC/kRSSCiam0eUSyaSvS8xUdzinEYeT0U3ZnEAe2EH5GQgKI84oNsS+En3Ef4iMSFWJ05xEHO41EkjuADwA79+xk7wf+LT/+2/uY/9lxZv7dr5Lt3gkzO8EFEVCVop0nsVbWsMAC1k4WfqOtqBYUCHW4Z7FYrHLYoqLIoWfEIWUVdL1BpbLoWVvhw3gFUWeowBrwDep3rBkpJ2mpGCWZhmio0UHItHFWTPNs6Hq7Hplh6I3vHTjHN24/wB8OZXWAAfDQQ4+x9JnP1XFFEs7xj7j8WKv6cuRsj7/47oucWoZi/hQ7r9uNThOyqclAZAhGUsjxGsPgqHYkUmyyGFciZkclvxrfRN2i7jRYhdYpWvcYGjpPkmgUCqMNS1f5qInV0DqkFhcgRZxSjAzWSr3FKOA48v9pFFncR5Dpr2cQJxKjiid5eeq4De+L4+GbaCY/YifHafo1nviaLv0UWmxQ7SLO8Xn6TjY+TtLvFYqpvmZ0Fb8zkhhiJFXQjySrxmvjfhDsMQu8iSAOawy791/H7IF9pLuuZfP7/gXji0uY63fDpilRYKhUnyJMf0mPBGMd9tyGb47pSy1zJ/DO42xVBz6VA+U1PdvDex1GAilSFKaqQqpNBSWBepXso+Y76FAzYTBKql+31uIouam6XtVAOjrBxME3D3zULj3Kv3n/E1TOYr1Ebj85doKXHvlxTWaAny8DdDSFm0YVe+c2s/P66+lsmsGMjAmhJO6oC87DhFunlU1XdQHRDdpKQT23yXnqmSX1H7U4vVizCrdmziucUzgP9z/6NN87/CwLSz/PNvVfPFqH1OICxE75nD6jbDVEdtpRZHEYRtQUJoBbEKdyGokWTiFRk1/x/pVOx7L2XWtEXJCi84pOJDoQRZ+CHdlwFTJob56+45hCnO8JJJpaCMfRdADxcx2DUkBnwmevjOKaUEikeQ3wNmBKwWSecu0bDzC0by/s2MHM3r34PIeZqbBYG1ngwnhuhY8lhZowocN4g4oKT4Xxvk6fKo80wbqqVrCpwqJW2CKUg+LdvSIvS3yqZKyBi301a0A3ih6BUfdaYUbGGDlwaGDbztEp/v25Fyltj8oJ1fkfHzrM93/wZJ8m72HBe5Z+Tj04YykcmlYc3L2VPfv3053dQjIyKg2xEdEhaRNSnqt9UqB3axWiSMJ7dEj1RQdP/28YMDnN2zePwzsVmOKK+x58ii9+46HLftxXGq1DanEBziALb/Nu9GKI/Ts/QhzSsIKZMRhOYCgRJYUlB8snQ+0cWVTOAf+H19Zw2xRmjUvTC4iqQrOeE4kK8XWWvjDqAv3UXFwbosxPhagxLDEYMVWNz1oLBkkVbg/Pb3zDdey9fjv5W26Gmc3ygjQVMoGLRfBVyAMqIbeQeoctFil7PYqiR3amxBYVi4vncL7C5iVOedwqfsI5T1EUcodtITGGqiqxRYF2Pig4GMgCucHoRiQQWGPOyq4ZIwuq0ci8oiDSOjAztz4AXqmoU2d2G9t++Tfx3obm2x4j+w6zaXKUyjmsc/R6Pb59+Gn+4r7vvaLPvlTYEhZOO84cX+DMiy+x2VqSlRFePFXNw7WNq9G6kLLzwV7N11XyWckaS7A1EiUnBnyK8hlpPoEiR9mOzGfagGgdUosLEFNQl4oc6GoYDan1SoEblkGgSQZZUOrf1YVUiaOywBkHP1yQSCrKEq3RrbEmVouyVtLWFZJm8wxSsqNeWRykF1Nwjr6IbEWfFv5yGKK/JI93Eia7KbfOTjNrEmYdTO/fw+T1c7BlBkZGB++MrRuMQBr77hUY4cThXAWuEiqwrcBWFL6H1xanhZTgjMdpJVMYkJqTdpLYUyF1po1BK4WzTsRXnQtUY9dPMdV+xA+mnHQo6CtNLcxaH3mM1Valkl0STNbBTF8DWIkifMFsscSBN92KsxbnLGVRcMobrj8sfDylFForzpxb4viZhb5ZX9E392EdnF+GhXPLnJtfwFdBIUOHY4uHF0gm9e9BxsdDv4YEfYWGgYhOofSKq7eZCo3KGcqD9hjTgcTjUyciuRsQrUNq8ZqgkUL9zi7cvk0YsM7D4qw4I5NB5xjkHm7fCVOjsHWTREmnzsML34ETtq94EBtNXyuay6GhPxAR+tTrZv0hMvjie1/glTlGDexD6mgZ8K92TXDHDZs5cNdv0RmbhEXQI2OiEadCv0kPcKV48F4iqZ+BGotvfL7EZjak6iyWJdOjSiqKvBIfEfTXnPH0coM14LyiUwyRkJLlGZ28Q5bnjI6OopTC2grtG8uAdVAUg0V6rfuU7ljEV7rhiOKjaY3Li5Gtc9zwr38Nb3vIBFjLxK5H2NFJMInBaEOW53z9H3/A//jat+v2g7Mv98FroLBwZAGee/EU4889x80LizBSScQSKfFAHSJZAjmkEeFWDYekw61Tc1utCt5AmsidXJM44uX7smwErxNSJaN9NiI25lG1+IUhkgLKHiydhIPbYOskpBNhoqqFbAaSCjZthskRGJsAjsJ4Bf/yOjhdwSkHZwo4WcDXT10+hYjYKwXiA6I6Qh7+5pAaUoySmsd1qZgCZpTiP9x0AxOp4sz8SW55883suHUf+dwOTGcIFivQOV4lOBsWoVSjTdpnpXkfFBHizq++sEs0oHHaY7XDacIIc4VSiShmO9G/9lqRmhytJKWmtMZoHUpAGp0m6CTps+VAhs/5Rj3J6MCmiKm7EM3paOG4n5eb8RUWe1eK6jZBl84B1jM5cw0H3/EulFFoozGpYXlkAq8MaWJwznPq/Hl+8OzP+Kcnn7/kb9XA6FDKjdvHOHDLXvbfup908wyMDAfHE6PAuI8hMRxo3j6QS2wlI9Gdc5gkISENWnse51wYLbgCSje4IfK5yvbwlVTPnnzhZ/zPv/7ffO/xp16dSdc5WofU4jXBI/WjUyU8fQq2Xwe7t4AdkjEuZ89LL6FOYGQnDI1ANgpuHkZ68K4bYN7CmRIWF+DZBfjWGSga/YOeV+eg4rIR790jKSChL9kTU3JrfX6TtBA/Lyak4vZZrdmbpbz/4F6mhgxPHf0xu247yJa3vhk2bxVR1Lyo5547W+GVQiUGZRKUEnFO5X0Q4mRFyizCh/0Qh+S1xwWHpLXCGRnmrbwicUntcJMkRykdyHEGbcSxKK0xWSYyQpF37b3M5XG+7xB9+HuUxfGRjde0yuXASpZa0LdwlTjqOOepEurg2OQ044em+9qmCQyNjrK9YzBZhnWOkydOkH77n3j0qRdk+mt46Vqp4Xg0I0Mp1++ZYt9Ne9h7y82kM5sg7ciUc1RwSqDCleDjFeEhjp+I2nOVrUi9J9Eaax3eOSpbkVzgkBSYFG+aV74LUbQktp8+eoz/+oWvvupWivWO1iG1uKz4+mNw/4+p5bisgzdtgW2j8MOTkGlJl/3anXDdDeD2g9M51ndxLuWlpxzFXS+xfD6sizbhaAVfWKzqcRCxbL5SeHMlIgvQIt+ZIESKBSSqm6avab0aRhHGYKS17wBuBPaHbTGieu9tt/DP33QLW95xB2ZihLGuJ9s6KzRuPN5WOCwlTnqETILyotG5Csl6EEFp2tkKW8niVpYlRVFQlSXeO7I8r5s1xelIjUhKELJwaq1J05Q0zTEmI0mS1esQgUQx2FejQqoJST2Zfm/R5Y+KXOPRQwRHfT+VaHtCCOgFdYRm6spaprZvYWTq3Sit8c5hFxc5p1MWjx7j2AtHKHs9Rjo5P1mseHKxfxuiEELOONIaYJXC1DcLCWS5pOuqimqpwJUVmRZlC4+X8R8NJ1SWZa3EbSuL9wXWWqytcNZR9Ao6eYdOtxO+X2GMQake2oemBu/keJWSxmMbFDA2MFqH1OKy4uySPJp4PoeihM5CIJY5uGNRUl0Tk5ANeZKOBxJ0rnjj28fonU+oqhRnR3nhbMV3v/szEiXSN2mScN5ZnlhaqCOWLn2NOpAFJtLWQ2AC9Gnhmr7Stqff4xPla8YQuvYOhK7uFOzL4eDYKAfHRzlZeUqToEbHOXDLAa69aT9s2w5jw6RjKX54CJ9k2OVlnFVYpyhROBRWeYxXl36XG0dH1B1YUNOF64VMyArOOfk9pOa00iQmwSQJWdZ3RKoumkciQpPSTYiAGky7psJATOVFYdGBaGmlgxos4q+9LSI6I4sMMIpMNaHB1w8bzlQcwwFASWI0yfgoOIlEfKKZ27aFN950A8c3jWNtxdjYGGMvnqF7/LTsgRIb2pOncWcXuGbIsHWsy/TmzXTHxtGdDkppOe4othpHUtTHLGw655w4nqqSuMmDcw7vPc46qqrCOkvZKzHaYIJDVUpJNKstSofRHl7YjwonxIZVKTwbC61DavFzx+ET8ohQwM7/Byfm4Z9NwNhcj9Fre0DGyPYR3vv5bWCn8XYW2M2JR+dJf/lL5Mkw3e4YQ2OjPLW8wJ8++RA5wm67Dkkd3h++wyAad8sIbbtZI4pacrFXifAZkwjlPQXeCtwGvAP4PmBTuG0L7HjrDey443ZYKGBonOS2d6CGJ6A7Bp1hWbBCC4lfhHPnlWSaICRo5O5X1bHRpUcY8S7aJAZTJeEO3mKMIUkT0iQVarf3kBjSJCdJc4wRJ94dEg30elJsTWNual/E/Ql6bcaExdA26N5hmy/Cy1d+RhOReN/cHhNmq8WGltohRcdT2f6jnqQaIgjtg3Ed2EKit1RLBFVV2KJg34E97Lh2u9gh7zB6zTZ++tAjPPXQIwDoxJANDfHXX/0m93/7H3nfji57927jLe9+L7N79pDNzAZ7GBjKSPIh6TY+dQqcR2Hw2uAN2OVlrK1CNGTlXIBEa9bS6/UGRkjEvyutSLKcXBkyXVEBynuMtWhKtC8lWnK/qBGZVwatQ2rxC4cHvnUYfnQM/v4ZyEYgH4ff/u1z7NlTgFkEdRJljoDdwuj2Kd7+h7+KMRMk6QRJNs3sC0f50Bd6dFPo5pq5sQ7Hz84z8ujjDGkYSjRbp6/hTK/HD0+eYBMwphVbJoc5Xlb8YH6ZdwAzecqet+yjs3mWfNs2vnXv33Pu2Anet2+O6+Z2sHf3bjY99jDeFszech2je3ZjrrsOKotKOqiODHujCqklBywT1BIcWI13BkuKQgrZifOkKNJUo43qr8tKNYgMIU214o64n5LToRYExmiMltpQkiYSuChDmmQkaYoOKTxjDMrICHOVhTHctZMkOBrVdzqR2h33YUCJIf5tZfquUeSv481YzYLa2QxU5kKnmA/bnRNihS2lfqSU9ORc4OuCFEXTXt4KKa0s8c6htSZJDFmeoJWSw7KW2R3byYdlmLzSBpN3uHN4nJvedDO7rp1lanaamf0H6IyPo4aGQ0OsVCCVrWTaYNYNRBSksVgHOzuHNTaQGyQqcs5hnR2Y8FpVFUprkixFaQMmpfJCxfduCbxF2R6JL7HlEp/9y7/mwR8+uaFjpNYhtbgiePRZefCwPNcafumXlpidjepzGsgYzt7J0PR2Dn74RtCb5MFmph9/Fv/Aw3S7is6wYfOWUY4dOcryE08xmSpG85Tp7XOcOH+ekbNnuFYrNieGfZunOLy4zLllyzu1Zt/4MG9/841ke/fgb74Z8+gTvFgUvP2m65i59VZmb38bcyMlfnEB3v52qQtNTkESEn2VkVRaVYa7ZUK2SdI3WIX3BkeCCXf/xmmMViSJFlWGOuOlhC0HYU23DX8Uy+ZeSA1Ko7UGD1qLk9Fa0nNy052QJElND46vMUlg1a1GG1ZKIgwT9OhqB+TkWEOKsE71DTikprOKabc4krCf0uq3FoeoqTZYgdSMkuCQeiEisGFfJW3llQ8RW/ieqHYQ7OWdODxXWTxe0mJakSbivHW4eRifmWFi+zbZPyXKCJOz09x26CBj27Zh8o44nBoV3mvZv6pEWQdpVhMYQhwUyCZyg+CDA3UhlRfZddEhWWvRVYXJMtAarxNJLTsr0Z6zUBV4SpaXz/OXf/MtfvLc0QvP2waC8ut8/u38/Dzj4+NXejda/AKwbRt0G2tAkhi+9MVP8oZbbgC/E9QmJLGWYAtD71gn1EsgsZrq6LOc/9a9mIlp9OgEZmyGpaNHOfHt+5mcnWVoYpxsx3Z6p46z8ONHGdqzh2zzLN2916Imx/HTU8wffRG71GN4aAjT6ZB0OnD0GL4osd0uThmc0mTJkjCsqoSqMlhryDpT6KBcYK3osC1mYL3DuwoVKBaprzBaphcYk4W740T6JlEhHRXcj/NyZ13J9NeiKOj1eiE15/ppPBPYcx4iVSLLc7I8ByO1inxoCIxQvy9AUO6uo6M09MJYK6t4qhtOKJfvUDAwvruOjKLDiZW76GS9kBRi822aB6dXyWhw15PXWispt+iQXBIW5x6+XJaoKdH9XYhitJXGuxKc2MhWlt5Sj6wzRN4dxlqLUoqk04W8i8q7gBEKaDaEMxpvDDoR6rxEYGEkPAZvrRArqh7KVWHOkZftZQ8qG4gm4nyrUEtaWFioIyNRyxCHpLXGGEM+NIROQyowSUOvUxHIG4sYSpYWz/HO//SH/PS5i40zXN84e/YsY2NjF31NGyG1WDc4cmTwuTGOf/jWsxw9ViG8uDG0HuOtb72dsZFpuls394vaFRjtyW99I354HLrDkI+SDA9hzp5laHqGbGwMNs+QT08xmjnYtQump2DzNHRzVJ4zPrctLOoaj8c5i9q0CXpChfYh+rHOINM8wTqHtQpTVnit8MhdsMOjrJZMk1tBGghUYecd2muUd/iBhTvcc8ff4xA7RU1gSHRSp+9iXUhphVLS35SkiaTpYlSkVD+OUY3vqlUY/MB313+vX1e/uU96GCAyuFUequ7PqR/xO5yNHiU4KS+Lv7WSrrN9urevHVIBvkIleX93QuOoEB8qYTWWJa6q8LbEVyWuktqLR+NthbIVvgqUFu1Bl+i0K2y6ODG3okHgCNpzcZJrPapDyBbehodv9CusOF8gTihC63jeVH/qR+hjUo3z4LzHOr/R+QxA65BarGNY6/nUp/5mYFun0+GBB+7nloOTsiGK2RlgZgbefSeUy5JCA9LZTYxfc62kX5IU8gTK87B7N4x0oZPCcAdUGMc31K3VEpzr4SqLsTmql2IKiy9lCutykWGdpVdJo6NyFapXopQTuZpQ40/LCzXHbCLuqMJisHgUiaWviLAGFGCMwafizBKT1AtdrFFE5lY21MWYFK0TkjwHrQedUT3CvOr3IK2FkJYCZPFWFUL9WPmelSk5S53Si/WhJqpGgT4u8lWgdRehgO9ClGUr6C3hekt4b0kS03eqNdGhwlcVtiyxy0ty7gDbW2LJViR5jjIG1QPtFboKxAodIkGj5RpRjXOgwxJZ1ScAbPN7HVhJETorkWw8Smstzjo5Z95jrSVdhbZtlCHctoQbGUiqYMcEXOkCEWLje6TWIbW4qlCWJX/8x/+FqclNodkJOmmHT//nT7N5yzRgKU2BVUtUS6DQZMPDmKQrqggdg0pDamoohyz23BRBHifI4jRG+jmtRQ1Aa0xiUMqybCtUmNzglcPj6fklyfLg0KFBVekwJpywkGuL1xalFUZnGKXDOAlXF8ids8RRBnGYnQ6NqabRHFtTt2UDKOrUnfwUSR3iHXp0PHWTK0FINRAYwmvr9F0dRRGCnUAgqNfrlam6GB244IBcI6qK0uNVo/YUUpNVJak8b6WbuqlW4YFKohxf9nBlKdFSutzYv4rYRKuwaGxtp0jM1nEfPFhKvDNgE3RMTRojX+YslOE4bAXK4JVpREEuCNGKgkSkpDsnGnsXQCG6gd6hrRBR6t4wI0xJk3fw2rCEQtWMRovyDu2s9OCalXW6jYnWIbW4qmCt5d57B6Om4aFhPvSbHyLJFJhlCs5S+kWKeegkI8yMXIM2GegMUtOnL3dSeQ6hyTJSkaUGo4L+t48ilwYUDqMUShuU8WjvcSHNUtoe3just2hl0GiMTqTRUSELuhKqstJK+oGIy4yr00LeOVxjto5wCZRw1ZSWYCZZvd9HBXKDUgod+pH6H6QGo6EYKUV7NJ1Q1K2LgqDR50CY/7MSjR6ZmD6LbLqY2vKSdsOkgV0Qt4VZuz44rKgT5x2esK0qJQ1XBlmjqtcgXdiB79beobwPKgqhWZj43OGoUF5qOpG1iJL0qAqsPe9dv36mpK7lbYWzFdrZ4JQCs9Kz4pwN/tQqkFDCjUJUzUiSjCTNIc+xSuGsRePQgcAhDslxruhxZmERu1L3bgOidUgtrnosLi3yK7/+K8IoU7LoeDzewXve9U7++5/+N5SRqMjEInZuRHo8ts8gXfcCByyhnCWpDHXnbI4IoZaKLM+xlQEKrFUoZenZHtY7rJUZRV5rehQSDRlE1qemSItTcFaaJk1ixJlZXxfArbWBsq2xNtCIfehZSQZTe84qXBhX4LXBWglELkjERcdTrSKWFB1TM2UVM44KBkdNrOWUqkbtp+iT8GJUURSQ5KK8iyiXUy3V9qiL+ZHQYC0Ui7iioDp/rmYTUsQeqEZUYgjvLXG2wFkv6r515GRCFxg4V+F8QdUDbRMy32i0NeFYKitpW+exvUV81YPeEsZ78c3eh3PmahKKrfr7U8WaEtQMyEjEMMZgsoQkTyBPUEqTV0lwSJJqxGpcVXD3F7/OPf/rPo6fOruKzTcWWofU4qqH955jL67OPvrh4R/xlb/9GpnOSVSKNik75nbw5je9ebAer6JWQ1xQ5O5axit4SaUExYR+4d/X8jDNSdWxriOvcOBVmOigJHUThknI+8UhKafwXuFcpAg7nLV4LWyy6LgANFroz00oiY5i860Lk2O9c7I97PMFbqRBmqgPIqYYxfs17BQbYOOGZkQUVLhd7B8KxATlg0NyNVMuhCPUDsn2qEegN9UYgkqDCJIGSreiT4ggRFDhXDjrcFWFK3tB2BSJZBGih4qEA6/D+2Rsu2QZE7AlvkoCAUXYc0II9JRFD2yJKitJwzWuvWhbH4kn8V/ncL4f1dSEhkh0oG9KpaRBVweOhUGo7soY5s8vcfTE6VWv742G1iG12ND4p4cf4cMf+fjAtt/4j7/BX33+r1bc5Mdb+cbIP23AZCgTUjNlJeKnoQu//wi9JT5Mdx0YIWHxXtWcAO+1NKuGvzln8TYy6lQIJGzd2a/inXeMkAiL4IoCd6QQW0z4HFun7owKwq3y5v6b6nEJzd/DaltPM20uETGyaxouUByrc2KfspTnUTmhuZ/OQm8pECmCUJO34pBMIBPESKox6K7ZTFrnDW0coSipNe8svaqgqirKskdkliRK6n/GJOBK6SVC15E0sTHXpkLd9gBJrUEX3d7yspAtjLWkOpR0mlZY4XziNhvqSvH8rAWlFCbVaAfaKgyZ1ASzTOqArxO0DqnF6w7f+b/f4QO/9YGBCOnd7343H/vYR5G0jaTpFLHGEhav2JdzESgiDVujtQqU335dR8aRO5RzWFtKFAOUpatTQM76wGlQeG+DFI1vMLAvdEjBb4VqSp9iHCnHykvs5MuyTykeUIZAIhIVNeQC4SAxDRJCM6Rc7eAje8/3CXfO4X1gzdngsJISlQQFCBd6fVSgscVIqOkoB9QgonrFKuchMs1jSUl5cUKVwqpQBNQerywYhTKQZBptEshS+X4byBieQCwJta2oGoEVxveKr2+2c3ondH6lFEabhjOl38Cc5ug0RWVZ6MfSKOtRXmSllLNSU9TZ6n1jGxTr3iGt877dFlchnn3uWZ597tmBbXme8+u//mvUCtN+kXp1K6xol/UsvuhBUVD0elhbSaOjlbtjW9ma6Wa0rhcSpfp3xwooE4Kj6U9gkpKOLLTSFxsW0LCtQbzr11EaiEVzS1I7pCRJSEMvkg7qAWQZKt7e1xJBDY26JCzOsTk3zfvptEbtSxAX7ArKhX5aDiuLd29JHIItYXEp1HcsJLq/DytnQBH+z9cOCapiGVeW2KVljLFo7YV/EfaA0ANUFL2ghh7sqhSJKdGmRKdBNUJryCtIElSSklegjAUb6Nyuv1s2MCadtyyePy+ECezLDmWXtGsjwqtsTWIx2mBMQpZrcWqVD5RyBUUkdFhp9vUWKOj1Ltd0sCuLS1nL171SwwsvvMDc3NyV3o0WLVq0aPEa8Pzzz7N9+/aLvmbdOyTnHE888QT79+/n+eeff1npiRZrY35+nrm5udaOlwGtLS8PWjtePqxXW3rvWVhYYOvWrRdE9iux7lN2Wmu2bdsGIHNM1pGhr1a0drx8aG15edDa8fJhPdryUvVIXz/VshYtWrRosa7ROqQWLVq0aLEucFU4pDzP+aM/+iPy/EKhyhaXjtaOlw+tLS8PWjtePmwEW657UkOLFi1atHh94KqIkFq0aNGixcZH65BatGjRosW6QOuQWrRo0aLFukDrkFq0aNGixbrAunZIjz32GIcOHWJycpLf//3fb3XtXgF+53d+py+sqRR79uwBWpteKl566SV27drFM888U2+7mO1au66N1Wy51vUJrS1Xw1e+8hV2795NkiS85S1v4fHHHwc23jW5bh1SURS8//3v57bbbuPBBx/k8OHDfP7zn7/Su3XV4KGHHuJrX/sap0+f5vTp0zz88MOtTS8RJ0+e5M477xxYQC9mu9aua2M1W8Lq1ye0tlwNP/3pT/nwhz/MZz7zGY4cOcLOnTv56Ec/ujGvSb9Oce+99/rJyUl//vx57733jzzyiL/jjjuu8F5dHSjL0o+OjvqFhYWB7a1NLw3vec97/Gc/+1kP+Kefftp7f3HbtXZdG6vZcq3r0/vWlqvhq1/9qr/77rvr59/85jd9lmUb8ppctxHSo48+yu23387Q0BAAN998M4cPH77Ce3V14Pvf/z7ee97whjfQ7XZ53/vex3PPPdfa9BLx53/+5/ze7/3ewLaL2a6169pYzZZrXZ/Q2nI13HnnnXziE5+onz/xxBPs2bNnQ16T69Yhzc/Ps2vXrvq5UgpjDKdPvz5G+b4WPP744xw4cIAvfelLHD58mDRNueuuu1qbXiJ27959wbaL2a6169pYzZZrXZ/Q/r9/OfR6Pf7kT/6ET37ykxvymly3DilJkgskMDqdDouLi1doj64efPCDH+SBBx7g0KFD7Nq1iz/7sz/j7/7u73DOtTZ9lbjY9dheq68Ma12f8/PzrS1fBp/+9KcZGRnh4x//+Ia8JtetQ5qamuLEiRMD2xYWFsiy7Art0dWLiYkJnHNs2bKltemrxMWux/ZafW2I1+fRo0dbW14E3/jGN/jc5z7HF7/4RdI03ZDX5Lp1SIcOHeKBBx6onz/zzDMURcHU1NQV3KurA5/61Kf48pe/XD//3ve+h9aagwcPtjZ9lbjY9dheq68Ma12fc3NzrS3XwFNPPcUHP/hB7r77bvbv3w9s0GvySrMq1kJZln5mZsbfc8893nvv77rrLn/nnXde4b26OnDPPff4PXv2+Pvvv9/fd999ft++ff4jH/lIa9NXCFYww9ayXWvXl0fTlmtdn963tlwNi4uL/sYbb/Qf+9jH/MLCQv3o9Xob7ppctw7Je6EudrtdPzs76zdt2uQfe+yxK71LVw3+4A/+wE9MTPi5uTn/u7/7u/7cuXPe+9amrwTNRdT7i9uutevFsdKWa12f3re2XIl7773XAxc8nn766Q13Ta778RNHjhzhwQcf5G1vexszMzNXenc2BFqbvnpczHatXS8fWlteOjbSNbnuHVKLFi1atHh9YN2SGlq0aNGixesLrUNq0aJFixbrAq1DatGiRYsW6wKtQ2rRokWLFusCrUNq0aJFixbrAq1DatGiRYsW6wKtQ2rRokWLFusCrUNq0aJFixbrAq1DatGiRYsW6wL/H2vKG3siADqMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示图片\n",
    "idx = 0 # 随机选择一张图片\n",
    "images = images.numpy()\n",
    "plt.imshow(images[idx].transpose((1,2,0))) # 转为(224, 224, 3)\n",
    "plt.title('label:'+str(labels[idx].item()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b466923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 图片标签索引\n",
    "label = labels[idx].item()\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab4c97d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'山楂'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 图片类别\n",
    "pred_classname = idx_to_labels[label]\n",
    "pred_classname  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92887aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 原始图像\n",
    "idx = 0\n",
    "mean = np.array([0.485, 0.456, 0.406])\n",
    "std = np.array([0.229, 0.224, 0.225])\n",
    "plt.imshow(np.clip(images[idx].transpose((1,2,0)) * std + mean, 0, 1))\n",
    "plt.title('label:'+ pred_classname)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2fc2882",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e331550",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import models\n",
    "import torch.optim as optim\n",
    "from torch.optim.lr_scheduler import StepLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "894c8c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=2048, out_features=80, bias=True)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 载入预训练模型,只修改最后一层全连接输出\n",
    "model = models.resnet50(pretrained=True) # 载入预训练模型\n",
    "\n",
    "# 修改全连接层，使得全连接层的输出与当前数据集类别数对应\n",
    "# 新建的层默认 requires_grad=True\n",
    "model.fc = nn.Linear(model.fc.in_features, 80)\n",
    "model.fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "73ec6c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainabel params23671952 / Total params: 23671952 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "def print_all_trainable_params(model):\n",
    "    total_params = 0\n",
    "    trainable_params = 0\n",
    "    for name, param in model.named_parameters():\n",
    "        num_params = param.numel()\n",
    "        total_params += num_params\n",
    "        if param.requires_grad:\n",
    "            trainable_params += num_params\n",
    "    \n",
    "    print(f\"Trainabel params{trainable_params} / Total params: {total_params} ({100 * trainable_params / total_params:.2f}%)\")\n",
    "\n",
    "print_all_trainable_params(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "584146bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainabel params11217552 / Total params: 11217552 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "def print_all_trainable_params(model):\n",
    "    total_params = 0\n",
    "    trainable_params = 0\n",
    "    for name, param in model.named_parameters():\n",
    "        num_params = param.numel()\n",
    "        total_params += num_params\n",
    "        if param.requires_grad:\n",
    "            trainable_params += num_params\n",
    "    \n",
    "    print(f\"Trainabel params{trainable_params} / Total params: {total_params} ({100 * trainable_params / total_params:.2f}%)\")\n",
    "\n",
    "print_all_trainable_params(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39782450",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.to(device)\n",
    "# 只微调训练最后一层全连接层的参数，其它层冻结\n",
    "# optimizer = optim.Adam(model.fc.parameters())\n",
    "# \n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
    "# 交叉熵损失函数\n",
    "criterion = nn.CrossEntropyLoss() \n",
    "\n",
    "# 训练轮次 Epoch\n",
    "EPOCHS = 50\n",
    "\n",
    "lr_scheduler = StepLR(optimizer, step_size=5, gamma=0.5) # 学习率衰减,step_size=5表示每5个epoch衰减一次，gamma=0.5表示衰减0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5690d9d4",
   "metadata": {},
   "source": [
    "# 模拟一个batch训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d3795a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得一个 batch 的数据和标注\n",
    "images, labels = next(iter(train_loader)) # iter() 是将一个可迭代对象转化为迭代器，然后使用next()函数获取迭代器的下一个元素。\n",
    "images = images.to(device)\n",
    "labels = labels.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c647c436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 81])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输入模型，执行前向预测，不学习用原始参数计算\n",
    "outputs = model(images)\n",
    "# 返回的是预测结果，维度为[batch_size, num_classes]\n",
    "# batch_size表示每一张图片，num_classes表示每个图片的类别数，里面放的是每个类别的概率\n",
    "outputs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "805f4d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(4.7689, device='cuda:0', grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由 logit，计算当前 batch 中，每个样本的平均交叉熵损失函数值\n",
    "loss = criterion(outputs, labels)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b53a1bb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 反向传播“三部曲”\n",
    "optimizer.zero_grad() # 清除梯度\n",
    "loss.backward() # 反向传播\n",
    "optimizer.step() # 优化更新"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87ee032b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得当前 batch 所有图像的预测类别\n",
    "_, preds = torch.max(outputs, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faf664ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([28,  3, 73, 11, 72, 20, 44, 56, 56, 56, 56, 56, 62, 29, 16, 56, 54, 77,\n",
       "        18, 30, 56, 62, 37, 48, 71, 20, 56, 30, 66, 74, 73, 16],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10389cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([46, 67, 19, 51, 23, 44,  4, 13,  9, 58, 25, 20, 28, 58, 40, 57, 25, 51,\n",
       "        51, 42,  8, 21, 54, 63, 68, 14, 27, 12, 65,  7, 80, 43],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceace286",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fac70d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e9fe103a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_one_batch(images, labels, epoch, batch_idx):\n",
    "    '''\n",
    "    运行一个 batch 的训练，返回当前 batch 的训练日志\n",
    "    '''\n",
    "    \n",
    "    # 数据移动到 GPU\n",
    "    images = images.to(device)\n",
    "    labels = labels.to(device)\n",
    "    \n",
    "    outputs = model(images) # 输入模型，执行前向预测\n",
    "    loss = criterion(outputs, labels) # 计算当前 batch 中，每个样本的平均交叉熵损失函数值\n",
    "    \n",
    "    # 优化更新权重\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    # 返回的是预测结果，维度为[batch_size, num_classes]\n",
    "    # batch_size表示每一张图片，num_classes表示每个图片的类别数，里面放的是每个类别的概率，\n",
    "    # max是求最大值，dim = 1 表示在第 1 个维度上求最大值，也就是求每个num_classes的最大值\n",
    "    _, preds = torch.max(outputs, 1) \n",
    "    # 将预测值拷贝到 CPU，转换为 NumPy 数组\n",
    "    preds = preds.cpu().numpy()\n",
    "    # 将损失值拷贝到 CPU，转换为 NumPy 数组\n",
    "    # loss.detach()将损失值从计算图中分离出来，返回一个不需要梯度的新张量\n",
    "    loss = loss.detach().cpu().numpy()\n",
    "    # 将预测结果和真实标签拷贝到 CPU，转换为 NumPy 数组\n",
    "    outputs = outputs.detach().cpu().numpy()\n",
    "    labels = labels.detach().cpu().numpy()\n",
    "    \n",
    "    log_train = {}\n",
    "    log_train['epoch'] = epoch\n",
    "    log_train['batch'] = batch_idx\n",
    "    # 计算分类评估指标\n",
    "    log_train['train_loss'] = loss # 损失值\n",
    "    log_train['train_accuracy'] = accuracy_score(labels, preds) # 计算准确率\n",
    "    log_train['train_precision'] = precision_score(labels, preds, average='macro') # 计算精确率，，average='macro'表示计算所有类别的精确率，并取平均值\n",
    "    log_train['train_recall'] = recall_score(labels, preds, average='macro') # 计算召回率\n",
    "    log_train['train_f1-score'] = f1_score(labels, preds, average='macro') # 计算 F1-score\n",
    "    \n",
    "    return log_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "39ad78d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_testset(epoch):\n",
    "    '''\n",
    "    在整个测试集上评估，返回分类评估指标日志\n",
    "    '''\n",
    "\n",
    "    loss_list = []\n",
    "    labels_list = []\n",
    "    preds_list = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for images, labels in test_loader: # 生成一个 batch 的数据和标注\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            outputs = model(images) # 输入模型，执行前向预测\n",
    "\n",
    "            # 获取整个测试集的标签类别和预测类别\n",
    "            _, preds = torch.max(outputs, 1) # 获得当前 batch 所有图像的预测类别\n",
    "            \n",
    "            preds = preds.cpu().numpy()\n",
    "            loss = criterion(outputs, labels) # 由 logit，计算当前 batch 中，每个样本的平均交叉熵损失函数值\n",
    "            loss = loss.detach().cpu().numpy()\n",
    "            outputs = outputs.detach().cpu().numpy()\n",
    "            labels = labels.detach().cpu().numpy()\n",
    "\n",
    "            loss_list.append(loss)\n",
    "            labels_list.extend(labels)\n",
    "            preds_list.extend(preds)\n",
    "        \n",
    "    log_test = {}\n",
    "    log_test['epoch'] = epoch\n",
    "    \n",
    "    # 计算分类评估指标\n",
    "    log_test['test_loss'] = np.mean(loss_list)\n",
    "    log_test['test_accuracy'] = accuracy_score(labels_list, preds_list)\n",
    "    log_test['test_precision'] = precision_score(labels_list, preds_list, average='macro')\n",
    "    log_test['test_recall'] = recall_score(labels_list, preds_list, average='macro')\n",
    "    log_test['test_f1-score'] = f1_score(labels_list, preds_list, average='macro')\n",
    "    \n",
    "    return log_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ce8f31f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch = 0\n",
    "batch_idx = 0 # 批次索引\n",
    "best_test_accuracy = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a98a4982",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练日志-训练集\n",
    "df_train_log = pd.DataFrame()\n",
    "log_train = {}\n",
    "log_train['epoch'] = 0\n",
    "log_train['batch'] = 0\n",
    "images, labels = next(iter(train_loader))\n",
    "log_train.update(train_one_batch(images, labels,epoch, batch_idx))\n",
    "df_train_log = df_train_log._append(log_train, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0cb1f903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>batch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_precision</th>\n",
       "      <th>train_recall</th>\n",
       "      <th>train_f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.393763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch  batch train_loss  train_accuracy  train_precision  train_recall  \\\n",
       "0      0      0   4.393763             0.0              0.0           0.0   \n",
       "\n",
       "   train_f1-score  \n",
       "0             0.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e8168241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练日志-测试集\n",
    "df_test_log = pd.DataFrame()\n",
    "log_test = {}\n",
    "log_test['epoch'] = 0\n",
    "log_test.update(evaluate_testset(epoch))\n",
    "df_test_log = df_test_log._append(log_test, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "986a66a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_precision</th>\n",
       "      <th>test_recall</th>\n",
       "      <th>test_f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.574569</td>\n",
       "      <td>0.010147</td>\n",
       "      <td>0.007555</td>\n",
       "      <td>0.010026</td>\n",
       "      <td>0.0054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch  test_loss  test_accuracy  test_precision  test_recall  test_f1-score\n",
       "0    0.0   4.574569       0.010147        0.007555     0.010026         0.0054"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a634432a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:57<00:00,  3.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.588.pth\n",
      "Epoch 2/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:46<00:00,  4.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.703.pth\n",
      "Epoch 3/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.754.pth\n",
      "Epoch 4/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.757.pth\n",
      "Epoch 5/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:46<00:00,  4.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.790.pth\n",
      "Epoch 6/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.832.pth\n",
      "Epoch 7/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:48<00:00,  4.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.838.pth\n",
      "Epoch 8/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:51<00:00,  4.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.846.pth\n",
      "Epoch 10/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:48<00:00,  4.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.853.pth\n",
      "Epoch 11/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.869.pth\n",
      "Epoch 12/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:56<00:00,  3.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:56<00:00,  3.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.875.pth\n",
      "Epoch 14/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:56<00:00,  3.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.876.pth\n",
      "Epoch 16/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.878.pth\n",
      "Epoch 17/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.879.pth\n",
      "Epoch 18/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.883.pth\n",
      "Epoch 19/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:48<00:00,  4.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.884.pth\n",
      "Epoch 20/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.886.pth\n",
      "Epoch 21/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.889.pth\n",
      "Epoch 22/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:48<00:00,  4.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:48<00:00,  4.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:48<00:00,  4.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.889.pth\n",
      "Epoch 26/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.890.pth\n",
      "Epoch 27/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.891.pth\n",
      "Epoch 28/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:57<00:00,  3.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:56<00:00,  3.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.891.pth\n",
      "Epoch 32/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:51<00:00,  4.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.893.pth\n",
      "Epoch 33/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.894.pth\n",
      "Epoch 35/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:47<00:00,  4.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:56<00:00,  3.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:54<00:00,  3.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存新的最佳模型 checkpoint_1/best-0.897.pth\n",
      "Epoch 39/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:57<00:00,  3.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:57<00:00,  3.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:59<00:00,  3.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 43/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 44/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 46/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:49<00:00,  4.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [01:58<00:00,  3.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [02:01<00:00,  3.69it/s]\n"
     ]
    }
   ],
   "source": [
    "# 开始训练\n",
    "for epoch in range(1, EPOCHS+1): \n",
    "    \n",
    "    print(f'Epoch {epoch}/{EPOCHS}')\n",
    "    \n",
    "    ## 训练阶段，一批一批的训练数据\n",
    "    model.train()\n",
    "    for images, labels in tqdm(train_loader): # 获得一个 batch 的数据和标注\n",
    "        batch_idx += 1 # 训练次数加1\n",
    "        log_train = train_one_batch(images, labels, epoch, batch_idx) # 训练一个 batch 的数据\n",
    "        df_train_log = df_train_log._append(log_train, ignore_index=True) # 将训练日志添加到训练日志数据框中\n",
    "        \n",
    "    lr_scheduler.step() # 更新学习率\n",
    "\n",
    "    ## 测试阶段\n",
    "    model.eval()\n",
    "    log_test = evaluate_testset(epoch)\n",
    "    df_test_log = df_test_log._append(log_test, ignore_index=True)\n",
    "    \n",
    "    # 保存最新的最佳模型文件\n",
    "    if log_test['test_accuracy'] > best_test_accuracy: \n",
    "        # 删除旧的最佳模型文件(如有)\n",
    "        old_best_checkpoint_path = 'checkpoint_1/best-{:.3f}.pth'.format(best_test_accuracy)\n",
    "        if os.path.exists(old_best_checkpoint_path):\n",
    "            os.remove(old_best_checkpoint_path)\n",
    "        # 保存新的最佳模型文件\n",
    "        best_test_accuracy = log_test['test_accuracy']\n",
    "        new_best_checkpoint_path = 'checkpoint_1/best-{:.3f}.pth'.format(log_test['test_accuracy'])\n",
    "        torch.save(model.state_dict(), new_best_checkpoint_path)\n",
    "        print('保存新的最佳模型', 'checkpoint_1/best-{:.3f}.pth'.format(best_test_accuracy))\n",
    "        # best_test_accuracy = log_test['test_accuracy']\n",
    "\n",
    "df_train_log.to_csv(r'checkpoint_1/训练日志-训练集.csv', index=False)\n",
    "df_test_log.to_csv(r'checkpoint_1/训练日志-测试集.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98baf054",
   "metadata": {},
   "source": [
    "# 测试一个epoch的准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "df2df3d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 50\n",
      "test_loss 0.37786004\n",
      "test_accuracy 0.896670001429184\n",
      "test_precision 0.8965746765713569\n",
      "test_recall 0.8900473046274968\n",
      "test_f1-score 0.8909605307243288\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 载入模型结构\n",
    "model = models.resnet18(pretrained=True) # 载入预训练模型\n",
    "model.fc = nn.Linear(model.fc.in_features, n_class)\n",
    "model.fc\n",
    "model  = model.to(device)\n",
    "\n",
    "\n",
    "# 加载模型权重\n",
    "state_dict = torch.load('checkpoint_1/best-{:.3f}.pth'.format(best_test_accuracy)) \n",
    "# state_dict = torch.load(r'checkpoint\\best-0.869.pth')\n",
    "model.load_state_dict(state_dict)\n",
    "model.eval()  # 设置模型为评估模式\n",
    "\n",
    "# 打印评估结果\n",
    "res = evaluate_testset(epoch)\n",
    "for k, v in res.items():\n",
    "    print(k, v)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
